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# Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess > Equal contributions. This work was done during an internship at KRAFTON.Co-advising. Abstract While reinforcement learning (RL) for large language models (LLMs) has shown promise in mathematical reasoning, strategic reasoning for LLMs using RL remains largely unexplored. We investigate whether LLMs can develop strategic reasoning capabilities through RL in chess. To this end, we leverage a chess-pretrained action-value network to provide dense reward on the LLM’s output move quality, which can be seen as a form of knowledge distillation. Our experiments show that our distillation-based dense rewards often outperform sparse binary rewards. However, surprisingly, all models plateau far below expert levels. We provide SFT and RL ablations on chess reasoning training and find evidence that this limitation stems from a deficit in the pretrained models’ internal understanding of chess—a deficit which RL alone may not be able to fully overcome. The code is available at https://github.com/krafton-ai/Chess-R1. 1 Introduction Reinforcement learning with verifiable rewards (RLVR) has shown strong performance in developing mathematical reasoning capabilities for large language models (LLMs) (Guo et al., 2025; Li et al., 2025; Yu et al., 2025). While these successes highlight LLMs’ capacity for logical thinking, a critical dimension of intelligence remains largely unexplored: strategic reasoning —the ability to plan, anticipate adversary actions, and make decisions in multi-agent environments. Beyond logical reasoning in static settings, strategic reasoning aligns more with real-world scenarios such as games, negotiation, and market competitions (Zhang et al., 2024; Park et al., 2025). To investigate this gap, we turn to chess, a strategic game demanding deep strategic reasoning abilities such as positional evaluation, long-term planning, and reasoning about an opponent’s intentions. In addition, chess offers a favorable environment for applying RLVR on LLMs, as it provides abundant publicly available game records and human-annotated reasoning about optimal moves. Given such a testbed for examining strategic reasoning, we raise the following research question: Can LLMs develop strategic reasoning capabilities through RLVR with chess? To this end, we train Qwen2.5 (Qwen: et al., 2025) and Llama3.1 (Grattafiori et al., 2024) models to predict the next best move in chess using GRPO (Shao et al., 2024). Unlike typical RLVR approaches that rely on sparse binary rewards (correct/incorrect), chess allows for dense reward signals: we leverage the fact that we can evaluate each move based on its estimated win probability, providing graded feedback proportional to the quality of the move. We implement this using a pre-trained chess expert as a reward model—a form of knowledge distillation from Q-value network to LLM—which evaluates position strength and provides dense continuous rewards rather than sparse binary ones. We conduct systematic experiments on a chess puzzle dataset, and observe the following: - Distillation-based dense rewards substantially outperforms sparse binary rewards in chess puzzle accuracy. - RL fine-tuning performs stronger than supervised fine-tuning (SFT); yet, its performance plateaus at 25-30 $\%$ puzzle accuracy—well below what is considered human expert performance (60–80 $\%$ ). - Additional reasoning distillation shows no RLVR gains, indicating the models fail to develop meaningful strategic reasoning despite further post-training. Through additional failure analysis, we find that base LLMs often struggle to grasp fundamental chess rules. Thus, we hypothesize that, in contrast to logical reasoning in math domains, the limited emergence of strategic reasoning in chess may stem from insufficient exposure to chess-specific knowledge during LLM pre-training. Our empirical findings also support recent claims that RL mainly amplifies existing capabilities of pre-trained LLMs (Li et al., 2025; Zhao et al., 2025b), and offer insight for practitioners aiming to elicit reasoning abilities in new environments: pre-trained domain knowledge is essential to develop advanced reasoning. 2 Post-training LLM on Chess <details> <summary>fig/chess_training_diagram.png Details</summary>  ### Visual Description ## Diagram: Chess AI System Architecture ### Overview The image presents a system architecture diagram for a chess-playing AI. It outlines the flow of information and processes from initial state input to reward model evaluation, involving components like a base model, policy model, and annotations. ### Components/Axes * **Input Text Box (Top-Left):** Contains the initial prompt, current FEN string, and legal moves. * Prompt: "You are a helpful assistant who plays chess professionally." * Current FEN string: "7R/7P/4p3/P1p5/8/2k5/7r/3K4 w - - 0 54" * Legal moves in SAN: "Rg8 Rf8 Re8 Rd8 Rc8 Rb8 Ra8 Kel Kcl a6" * **State Storage (Top-Center-Left):** A cylinder representing state storage, labeled with states s1, s2 (highlighted in orange), and s3. * **Annotations (Top-Center):** A box labeled "Annotations" with the sub-label "o3 reasoning". It is connected to a stack of papers. * **SFT (Optional) (Center):** Indicates Supervised Fine-Tuning as an optional step. * **Base Model (Center-Left):** A yellow box labeled "Base Model" with the sub-label "Qwen/Llama". * **Policy Model (Center):** A yellow box labeled "Policy Model" with the sub-label "Base or SFT". * **Reward Model (Right):** A blue box labeled "Reward Model" with the sub-label "Critic Q(s, a)". * **Actions (Right):** Three white boxes labeled a(1), a(2), and a(3). * **Rewards (Right):** Equations defining rewards r(1), r(2), and r(3) as Q(s, a(1)), Q(s, a(2)), and Q(s, a(3)) respectively. * **GRPO (Top-Right):** Label for the connection between the Policy Model and the Actions. ### Detailed Analysis or ### Content Details * **Flow:** * The initial state (s1, s2, s3) is fed into the Annotations. * Annotations and the initial state are fed into the Base Model. * The Base Model feeds into the Policy Model. * Optionally, SFT can be applied to the Base Model and Policy Model. * The Policy Model outputs actions a(1), a(2), and a(3). * These actions, along with the state, are used by the Reward Model to calculate rewards r(1), r(2), and r(3). * **Data Points:** * The FEN string represents a specific chess position. * The legal moves are given in Standard Algebraic Notation (SAN). * The reward model uses a critic Q function to evaluate state-action pairs. ### Key Observations * The system uses a combination of a base model and a policy model, potentially fine-tuned with supervised learning. * Annotations and reasoning are incorporated into the process. * The reward model evaluates the quality of actions based on a critic function. ### Interpretation The diagram illustrates a reinforcement learning approach to training a chess-playing AI. The system takes a chess position as input, uses a base model to generate potential moves, refines these moves with a policy model, and then evaluates the moves using a reward model. The optional SFT suggests that supervised learning is used to pre-train or fine-tune the models. The annotations component indicates that additional information or reasoning is incorporated into the decision-making process. The use of a critic function in the reward model suggests a value-based reinforcement learning approach. </details> (a) Chess Training Example. (b) Post-training with GRPO. Figure 1: Overview: (a) A data sample from the Lichess puzzle dataset is formatted into a prompt that includes the current board state and the set of legal moves. (b) At each GRPO step, the policy model generates multiple rollouts of predicted actions. A reward model evaluates these rollouts with dense feedback, including sub-optimal actions. Optionally, reasoning traces from OpenAI o3 are fine-tuned into the base model before RL. 2.1 Chess Dataset Training Data. We use Lichess puzzles collected from the Lichess Puzzle Database https://database.lichess.org as our training dataset. Each puzzle contains an initial board position along with a list of sequential moves which, when played in order, leads to a tactical win. To create our training dataset, we decompose each puzzle’s solution trajectory into individual position-action pairs. Formally, each puzzle is denoted as a trajectory $g^{(i)}=(s^{(i)}_{0},a^{*(i)}_{0},s^{(i)}_{1},a^{*(i)}_{1},...,s^{(i)}_{T_{i}})$ , where $i∈\{1,...,N\}$ indexes the $i$ -th puzzle, $s^{(i)}_{t}$ is the board position at time step $t$ , $a^{*(i)}_{t}$ is the optimal move, and $T_{i}$ is the trajectory length. We construct our position-diverse training dataset by aggregating all such pairs: $\mathcal{D}=\{\,(s^{(i)}_{t},\,a^{*(i)}_{t})\mid i∈\{1,...,N\},\;0≤ t<T_{i}\}\!$ , resulting in a total of 19.2k training samples for our dataset $\mathcal{D}$ . Prompt template. We require a textual interface which LLMs can reason about chess positions. Based on our collected chess dataset $\mathcal{D}$ , we adopt a concise prompt format using Forsyth–Edwards Notation (FEN) (Edwards, 1994) format for board states and Standard Algebraic Notation (SAN) format for moves. Alternative representations of chess data, such as full move histories in Portable Game Notation (PGN) or Universal Chess Interface (UCI) move notation, did not yield measurable improvements. We also require the model to place reasoning in <think> tags and answers in <answer> tags (Guo et al., 2025; Zhao et al., 2025a). See Appendix A, C.1 for qualitative examples and ablation studies on prompt formatting. 2.2 Reinforcement Learning Fine Tuning To train LLMs with RL on chess, we adopt GRPO (Shao et al., 2024) for policy improvement, which has recently been shown to be effective by contrasting multiple rollouts of the same prompt (Guo et al., 2025). Specifically, we employ two alternative reward schemes: - Sparse reward: Binary indicator whether predicted move $\hat{a}_{t}$ matches optimal answer $a_{t}^{\star}$ : $$ r_{\text{sparse}}\;=\;\mathbf{1}[\,\hat{a}_{t}=a_{t}^{\star}\,]. $$ - Dense reward: A dense real-valued score provided by a pre-trained action-value network $Q_{\theta}(s,a)$ from Ruoss et al. (2024) that predicts post-move win probability: $$ r_{\text{dense}}\;=\;Q_{\theta}(s_{t},a_{t}),\qquad Q_{\theta}(s,a)\in[0,1]. $$ The critic $Q_{\theta}(s,a)$ is a 270 M-parameter, 16-layer decoder-only Transformer (Vaswani et al., 2017) trained for 10 M optimization steps on 15B Stockfish annotated state-action pairs with the HL-Gauss loss (Imani & White, 2018). Querying this expert network during RL fine-tuning can be viewed as a form of knowledge distillation (Zhang et al., 2025c): dense win-rate evaluations inject the teacher’s strategic insight, guiding the student for not only optimal moves, but sub-optimal moves also. The critic itself attains 2299 ELO and achieves 95.4% accuracy on Lichess puzzles. We also include two auxiliary binary rewards: (i) $r_{\text{fmt}}∈\{0,1\}$ for proper tag formatting (<think> for reasoning and <answer> for answers), and (ii) $r_{\text{lang}}∈\{0,1\}$ to enforce that the output is in English. The total training reward at step $t$ is therefore $r_{t}=\lambda_{\text{sparse}}r_{\text{sparse}}+\lambda_{\text{dense}}r_{\text{dense}}+\lambda_{\text{fmt}}r_{\text{fmt}}+\lambda_{\text{lang}}r_{\text{lang}}$ . We set $(\lambda_{\text{sparse}}=1,\lambda_{\text{dense}}=0)$ for the sparse reward setting and $(\lambda_{\text{sparse}}=0,\lambda_{\text{dense}}=1)$ for the dense reward setting. 3 Experiments We now present a series of experiments designed to evaluate post-training LLM on chess with RLVR. Further details regarding experiment setup are provided in Appendix B. 3.1 RL Fine-tuning <details> <summary>fig/chess_rl_training.png Details</summary>  ### Visual Description ## Line Charts: Lichess Puzzle Accuracy vs. Training Step for Different Models ### Overview The image presents three line charts comparing the performance of different language models (Qwen2.5-3B, Qwen2.5-7B, and Llama3.1-8B) on a Lichess puzzle accuracy task. The charts depict the accuracy achieved by each model over training steps, using different reward strategies: dense reward, sparse reward, and Action SFT (Supervised Fine-Tuning). ### Components/Axes * **Titles (Top of each chart):** * Left Chart: Qwen2.5-3B * Middle Chart: Qwen2.5-7B * Right Chart: Llama3.1-8B * **Y-axis (Vertical):** Lichess Puzzle Acc, ranging from 0.00 to 0.25 (left), 0.00 to 0.30 (middle), and 0.00 to 0.30 (right). * **X-axis (Horizontal):** Training Step, ranging from 0 to 150 in increments of 30. * **Legend (Bottom-left of the first chart):** * Blue line: Dense reward * Gray line: Sparse reward * Red dotted line: Action SFT ### Detailed Analysis **Chart 1: Qwen2.5-3B** * **Dense reward (Blue):** The line starts at approximately 0.00 and increases sharply until around training step 90, reaching an accuracy of approximately 0.22. It then plateaus, reaching a final accuracy of approximately 0.24 at training step 150. * (0, 0.005), (30, 0.015), (60, 0.055), (90, 0.16), (120, 0.225), (150, 0.24) * **Sparse reward (Gray):** The line remains relatively flat near 0.00 throughout the training steps. * (0, 0.005), (150, 0.005) * **Action SFT (Red dotted line):** The line is horizontal at approximately 0.17. * (0, 0.17), (150, 0.17) **Chart 2: Qwen2.5-7B** * **Dense reward (Blue):** The line starts at approximately 0.00 and increases sharply until around training step 60, reaching an accuracy of approximately 0.24. It then plateaus, reaching a final accuracy of approximately 0.29 at training step 150. * (0, 0.005), (30, 0.12), (60, 0.24), (90, 0.275), (120, 0.285), (150, 0.29) * **Sparse reward (Gray):** The line starts at approximately 0.01 and increases sharply until around training step 60, reaching an accuracy of approximately 0.29. It then plateaus, reaching a final accuracy of approximately 0.31 at training step 150. * (0, 0.01), (30, 0.20), (60, 0.29), (90, 0.30), (120, 0.31), (150, 0.315) * **Action SFT (Red dotted line):** The line is horizontal at approximately 0.19. * (0, 0.19), (150, 0.19) **Chart 3: Llama3.1-8B** * **Dense reward (Blue):** The line starts at approximately 0.00 and increases sharply until around training step 60, reaching an accuracy of approximately 0.30. It then fluctuates between 0.28 and 0.32, reaching a final accuracy of approximately 0.32 at training step 150. * (0, 0.005), (30, 0.11), (60, 0.30), (90, 0.28), (120, 0.31), (150, 0.32) * **Sparse reward (Gray):** The line remains relatively flat near 0.01 throughout the training steps. * (0, 0.01), (150, 0.01) * **Action SFT (Red dotted line):** The line is horizontal at approximately 0.18. * (0, 0.18), (150, 0.18) ### Key Observations * For all three models, the dense reward strategy (blue line) results in a significant increase in Lichess puzzle accuracy compared to the sparse reward strategy (gray line). * The sparse reward strategy (gray line) shows minimal improvement in accuracy across all models. * The Action SFT (red dotted line) provides a baseline accuracy level, which the dense reward strategy surpasses for all models. * The Qwen2.5-7B model shows the highest accuracy with the sparse reward strategy, nearly matching the dense reward performance. * The Llama3.1-8B model achieves the highest overall accuracy with the dense reward strategy, reaching approximately 0.32. ### Interpretation The data suggests that a dense reward strategy is more effective for training language models on Lichess puzzle accuracy compared to a sparse reward strategy. The dense reward provides more frequent feedback to the model during training, leading to faster and more significant improvements in accuracy. The Action SFT baseline indicates the performance level achievable through supervised fine-tuning alone, which the dense reward strategy consistently outperforms. The Qwen2.5-7B model's performance with the sparse reward suggests that certain models may be more sensitive to reward structure. The Llama3.1-8B model's superior performance with the dense reward indicates its potential for high accuracy in this task. </details> Figure 2: Evaluation performance comparison of RL fine-tuned models. We fine-tuned Qwen2.5-3B, Qwen2.5-7B and Llama3.1-8B on our Lichess position-action pair dataset $\mathcal{D}$ . For each model, we trained three variants: one with sparse rewards, one with dense expert guidance, and one baseline variant fine-tuned directly via supervised learning (SFT) on optimal actions, without RL. The supervised baseline helps evaluate performance when no explicit reasoning is involved. Figure 2 shows that dense reward models generally outperform sparse reward variants, with sparse rewards completely failing for Qwen2.5-3B and Llama3.1-8B. Dense reward models also surpass SFT baselines, demonstrating the value of reasoning over direct action prediction. However, all models plateau around 25-30% puzzle accuracy, well below expert performance (1800 ELO models achieve 66.5% (Ruoss et al., 2024)). Qualitative analysis (Appendix D) reveals that while models show structured reasoning and systematic move exploration, they lack strategic coherence and long-term evaluation. In summary, although RL improves tactical reasoning particularly with expert-guided feedback, achieving expert-level strategic chess understanding from scalar rewards remains challenging for LLMs. <details> <summary>fig/chess_reasoning_training.png Details</summary>  ### Visual Description ## Line Graphs: Lichess Puzzle Accuracy vs. Training Step for Qwen2.5-7B and Llama3.1-8B ### Overview The image presents two line graphs comparing the performance of two language models, Qwen2.5-7B and Llama3.1-8B, on a Lichess puzzle accuracy task. Each graph plots the accuracy against the training step, with two lines representing training with and without reasoning (SFT - Supervised Fine-Tuning). ### Components/Axes * **Titles:** * Left Graph: Qwen2.5-7B * Right Graph: Llama3.1-8B * **Y-axis (Lichess Puzzle Acc):** * Label: Lichess Puzzle Acc * Scale: 0.00 to 0.30, with increments of 0.05 (0.00, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30) * **X-axis (Training Step):** * Label: Training Step * Scale: 0 to 150, with increments of 30 (0, 30, 60, 90, 120, 150) * **Legend (located in the center of the two graphs):** * Blue line: w/ Reasoning SFT * Gray line: w/o Reasoning SFT ### Detailed Analysis **Left Graph: Qwen2.5-7B** * **Blue Line (w/ Reasoning SFT):** * Trend: Generally increasing, with a slight plateau after 90 training steps. * Data Points: * 0 Training Step: ~0.21 * 30 Training Step: ~0.22 * 60 Training Step: ~0.25 * 90 Training Step: ~0.28 * 120 Training Step: ~0.28 * 150 Training Step: ~0.29 * **Gray Line (w/o Reasoning SFT):** * Trend: Rapidly increasing initially, then plateaus after 90 training steps. * Data Points: * 0 Training Step: ~0.01 * 30 Training Step: ~0.07 * 60 Training Step: ~0.19 * 90 Training Step: ~0.28 * 120 Training Step: ~0.29 * 150 Training Step: ~0.29 **Right Graph: Llama3.1-8B** * **Blue Line (w/ Reasoning SFT):** * Trend: Increasing, then plateaus after 60 training steps. * Data Points: * 0 Training Step: ~0.21 * 30 Training Step: ~0.24 * 60 Training Step: ~0.26 * 90 Training Step: ~0.27 * 120 Training Step: ~0.27 * 150 Training Step: ~0.29 * **Gray Line (w/o Reasoning SFT):** * Trend: Rapid initial increase, followed by fluctuations and a plateau. * Data Points: * 0 Training Step: ~0.01 * 30 Training Step: ~0.31 * 60 Training Step: ~0.28 * 90 Training Step: ~0.31 * 120 Training Step: ~0.33 * 150 Training Step: ~0.33 ### Key Observations * For both models, training with reasoning (blue line) generally results in higher initial accuracy. * The "w/o Reasoning SFT" (gray line) for both models shows a steeper initial increase in accuracy compared to "w/ Reasoning SFT" (blue line). * Llama3.1-8B (right graph) shows more fluctuation in the "w/o Reasoning SFT" line compared to Qwen2.5-7B (left graph). * Both models reach similar accuracy levels at the end of the training steps, regardless of whether reasoning is included in the SFT. ### Interpretation The graphs suggest that while training with reasoning (SFT) provides a better starting point for both models, training without reasoning catches up as the training progresses. The fluctuations in Llama3.1-8B's "w/o Reasoning SFT" line might indicate instability or sensitivity to the training data. The similar final accuracy levels suggest that both models can achieve comparable performance on the Lichess puzzle task, regardless of the initial training approach. The data indicates that reasoning SFT is more important in the early stages of training. </details> Figure 3: Evaluation performance of models trained with reasoning SFT followed by RL fine-tuning. | Qwen2.5-3B Qwen2.5-3B-It Qwen2.5-7B | 0.0% 0.0% 0.0% | 35.8% 53.7% 42.7% | | --- | --- | --- | | Qwen2.5-7B-It | 0.0% | 52.0% | | Llama3.1-8B | 0.0% | 12.7% | | Llama3.1-8B-It | 0.0% | 52.4% | Table 1: LLM performance on chess knowledge diagnostic tasks 3.2 Reasoning SFT To probe the cause of the RL plateau, we asked whether domain-specific reasoning traces from advanced reasoning models could enable LLMs better leverage RL signals for chess skill acquisition and overcome the previously observed performance ceiling. To do so, we curated a reasoning SFT corpus of 1k high-quality reasoning traces generated by OpenAI o3, featuring candidate move evaluation, tactical assessment, and strategic justifications (See Appendix B.2 for details). After performing SFT on Qwen2.5-7B and Llama3.1-8B with this corpus, we applied our GRPO RL pipeline with expert critic $Q_{\theta}(s,a)$ feedback. While LLMs produced markedly more comprehensive reasoning traces after reasoning SFT, Figure 3 shows that they disappointingly exhibited similar puzzle accuracy plateaus when subsequently trained with RL. In fact, Llama3.1-8B’s performance actually declined relative to its non-SFT baseline. These results raise questions about whether the reason behind the chess performance plateau stems not from inadequate reasoning abilities, but from insufficient chess knowledge. Without extensive domain knowledge from pre-training, RL alone may not provide the chess-specific understanding that strategic play demands. 3.3 Failure Analysis To study whether the performance limitation of RLVR for chess stems from inadequate chess domain knowledge, we evaluate LLMs on two diagnostic tasks that require basic and essential but non-trivial understanding of chess rules. Board–state comprehension: Given a FEN string and a legal sequence of one to five SAN moves, the model predicts the resulting position in FEN. The model must have a faithful internal simulator of chess rules to be successful in this task. MATE puzzle: Each instance in the MATE dataset (Wang et al., 2024) presents a mid-game position together with two candidate moves. The model must identify the superior move through tactical evaluation. As summarized in Table 1, all tested LLMs show poor performance on both diagnostics, confirming inadequate internal models of chess state transitions and tactics. We tested both Base and Instruct variants—Instruct models for better formatting compliance, though they share the same underlying knowledge as their Base counterparts. This shortfall strongly supports our earlier hypothesis: current models lack fundamental understanding of chess, preventing them from developing high performance through reward optimization alone. Since models cannot reliably track game states or recognize elementary tactics, reward optimization fails to develop high-quality strategic reasoning and elicit expert-level play. 4 Related Work RLVR for LLM reasoning. RLVR (Lambert et al., 2024) has emerged as a powerful paradigm to elicit LLM reasoning for tasks with deterministic evaluation criteria. Notably, in mathematical domains, simple rule-based rewards have proven surprisingly effective without requiring complex reward models (Guo et al., 2025; Yu et al., 2025; Wang et al., 2025), achieving performance comparable to, or even surpassing, that of humans on mathematical olympiads such as AIME. Furthermore, some works have extended the application of RLVR to other domains as well (Liu et al., 2025b; Zhang et al., 2025a; Gurung & Lapata, 2025). Despite these successes, fundamental questions about RLVR’s success remain underexplored. Recent studies suggest that its success often originates from abundant knowledge already present in base models rather than being genuinely learned through RL (Liu et al., 2025a; Zhao et al., 2025b; Li et al., 2025; Shao et al., 2025; Yue et al., 2025). A systematic investigation across models, tasks, RL algorithms, and scale remains necessary to fully understand LLM capabilities and its limitations in developing genuine reasoning abilities through RL. LLMs in chess. Recent work explores how LLMs can tackle chess tasks, complementing traditional chess engines. While specialized networks like AlphaZero (Silver et al., 2017) achieve superhuman performance via self-play and search, they lack human-readable insight. Conversely, LLMs can explain moves in natural language but often lack precise strategic understanding, leading to hallucinations or illegal moves. To bridge this gap, researchers have integrated chess engines with LLMs for fluent move commentary (Kim et al., 2025) and developed chess-centric models like ChessGPT (Feng et al., 2023) through domain-specific pre-training. Fine-tuning on expert rationales from the MATE dataset (Wang et al., 2024) enables Llama models to outperform GPT-4 and Claude at binary move selection. Most notably, ChessLLM (Zhang et al., 2025b) demonstrates that an LLM fine-tuned on 20B tokens of complete games can play chess at an expert level, attaining an Elo of 1788 without external search; however FEN-centric training risks overfitting and degrading natural language responses into fragmented notations. Despite these advances, existing approaches primarily rely on supervised learning from human games or engine guidance rather than autonomous strategic development. Whether LLMs can develop genuine chess reasoning through RL remains largely unexplored, particularly in understanding if such learning produces transferable strategic insights or merely pattern memorization. Our work addresses this gap using chess as a controlled testbed for studying autonomous strategic development in LLMs. 5 Conclusion & Limitations We investigated whether LLMs can develop strategic reasoning through RLVR on chess, introducing a novel approach that utilizes dense rewards from pretrained action-value networks. While dense rewards often outperform sparse ones, all models plateau well below human experts, revealing fundamental limitations in current post-training approaches. Surprisingly, despite producing more structured reasoning when trained on advanced traces from OpenAI o3, models still yield similar performance plateaus when subsequently trained with RL. Our failure analysis reveals a potential root cause: current LLMs demonstrate inadequate internal chess knowledge. These results raise speculation that RL cannot overcome impoverished domain knowledge. While RL excels at optimizing behavior toward rewards, LLMs cannot learn de novo the foundational knowledge necessary for strategic thinking when absent from pretraining. We hypothesize that prior RLVR successes in math domains stem from rich pretraining exposure, unlike chess which receives minimal coverage. Our findings align with recent work showing that RL only amplifies existing capabilities (Li et al., 2025; Liu et al., 2025a; Yue et al., 2025). For strategic reasoning in new domains, adequate domain knowledge during pretraining is essential—post-training RL alone is insufficient. References - Edwards (1994) Steven J. Edwards. Standard: Portable game notation specification and implementation guide, 1994. URL https://ia802908.us.archive.org/26/items/pgn-standard-1994-03-12/PGN_standard_1994-03-12.txt. - Feng et al. (2023) Xidong Feng, Yicheng Luo, Ziyan Wang, Hongrui Tang, Mengyue Yang, Kun Shao, David Mguni, Yali Du, and Jun Wang. 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Llamafactory: Unified efficient fine-tuning of 100+ language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), Bangkok, Thailand, 2024. Association for Computational Linguistics. URL http://arxiv.org/abs/2403.13372. Appendix A Input prompt format For our main experiments, we employ the input prompt template shown in Figure 4 for both training and evaluation. This prompt format consists of several key components: (i) the current board state represented in Forsyth–Edwards Notation (FEN), (ii) the complete set of legal moves available from the current position written in Standard Algebraic Notation (SAN), and (iii) structured instructions that guide the model to produce reasoning enclosed in <think> tags followed by the chosen move in <answer> tags. Additionally, the prompt includes a concise reminder of fundamental chess rules to support the model’s reasoning process. This comprehensive prompt design ensures that the model has access to all necessary information about the current game state while maintaining a consistent format that facilitates both learning during training and reliable evaluation during testing. Input prompt template A conversation between User and Assistant. The User asks the best move to make for a given chess board state, and the Assistant solves it. The Assistant is a professional chess player who first thinks about the reasoning process in the mind and then provides the User with the answer. The Assistant’s reasoning process and answer must be enclosed within <think> </think> and <answer> </answer> tags, respectively. The reasoning process should describe how the Assistant analyzes the position and decide on the best move, including: - A strategic evaluation of the position. - A comparison of key candidate moves. - For each candidate, consider the opponent’s likely response and outcome. - Conclude with a clear justification for your final choice. The answer must be in SAN notation, strictly using the moving piece and the destination square (e.g., Nf3, Rxf2, c5). Reminder of chess rules: - Bishops move diagonally. - Rooks move horizontally or vertically. - Knights jump in an L-shape. - Queens combine rook and bishop movement. - Kings move one square in any direction. - Pawns move forward, capture diagonally, and can promote. User: The current FEN string is <fen> and legal moves are <san_1> <san_2> …<san_L>. What is the best move to make out of the list of legal moves? Assistant: Let me solve this step by step. <think> Figure 4: Input prompt format for chess reasoning tasks with FEN for board state and SAN notation for move action. This prompt structure was used throughout our main studies. Appendix B Experiment Setup B.1 Lichess Puzzle Dataset Training data collection. We collect Lichess puzzles spanning solver ratings from 200 to 2800 Elo from the Lichess Puzzle Database. Each puzzle contains an initial board position and a sequence of moves that leads to a tactical victory. To create position-action training pairs, we decompose each puzzle’s solution trajectory, where a puzzle with $T$ moves generates $T$ individual training samples. This decomposition process yields a total of 19.2k training samples for our dataset $\mathcal{D}$ . Evaluation protocol. We evaluate chess understanding through puzzle-solving capability using a held-out evaluation dataset of 10K Lichess puzzles across various difficulty levels from (Ruoss et al., 2024). Our evaluation metric is puzzle accuracy, defined as the percentage of puzzles where the model’s complete action sequence exactly matches the ground-truth solution sequence. This strict evaluation criterion ensures that models must not only identify good moves but solve puzzles entirely and correctly, providing a comprehensive assessment of chess reasoning ability. To ensure consistency, we use the same prompt template during evaluation as in training. B.2 Reasoning Trace Collection We used OpenAI’s o3 model (reasoning: high, summary: detailed) to collect sophisticated chess reasoning data samples for our reasoning SFT experiments. We provide an example of the synthetic o3 reasoning trajectory in Figure 23, 24. From o3 reasoning traces, we observed fewer hallucinations about spatial positioning and reasoning compared to our base models, while evaluating strategic positions in terms of both breadth and width. To build the SFT corpus, we sample 1,000 chess problems from our larger collection, balancing them evenly across the entire ELO range. We create each SFT example by concatenating o3 reasoning summaries and final generation to each problem. This yields a high-quality chess SFT dataset, which we expected would strengthen our model’s strategic reasoning capability by distilling structured rationale before subsequent RL training. We include the token length distribution of the SFT data in Figure 5. <details> <summary>fig/o3_reasoning_token_distributions_qwen_vs_llama.png Details</summary>  ### Visual Description ## Histogram: OpenAI o3 Reasoning Data Token Length Distributions ### Overview The image presents two histograms comparing the token length distributions of the Qwen2.5 and Llama3.1 tokenizers on OpenAI o3 reasoning data. The histograms display the frequency of different token counts, with vertical red dashed lines indicating the mean token length for each tokenizer. ### Components/Axes * **Title:** OpenAI o3 Reasoning Data Token Length Distributions * **X-axis (Token Count):** Ranges from 400 to 1600 in increments of 200. * **Y-axis (Frequency):** Ranges from 0 to 175 in increments of 25. * **Left Histogram:** * Title: Qwen2.5 Tokenizer * Bar Color: Light Blue * Mean: 974.0 (indicated by a red dashed line) * **Right Histogram:** * Title: Llama3.1 Tokenizer * Bar Color: Light Green * Mean: 965.4 (indicated by a red dashed line) * **Legend:** Each histogram has a legend indicating the mean token length, represented by a red dashed line. ### Detailed Analysis **Left Histogram (Qwen2.5 Tokenizer):** * The distribution is approximately normal, centered around 974. * The frequency starts to increase around a token count of 600. * The peak frequency occurs around a token count of 900-1000, with a frequency of approximately 130. * The frequency decreases gradually as the token count increases beyond 1000. * At a token count of 1600, the frequency is close to 0. **Right Histogram (Llama3.1 Tokenizer):** * The distribution is approximately normal, centered around 965.4. * The frequency starts to increase around a token count of 600. * The peak frequency occurs around a token count of 900-1000, with a frequency of approximately 170. * The frequency decreases gradually as the token count increases beyond 1000. * At a token count of 1600, the frequency is close to 0. ### Key Observations * Both tokenizers exhibit similar token length distributions, with the majority of token counts falling between 600 and 1400. * The mean token length for Qwen2.5 (974.0) is slightly higher than that of Llama3.1 (965.4). * The Llama3.1 tokenizer has a slightly higher peak frequency than the Qwen2.5 tokenizer. ### Interpretation The histograms suggest that both Qwen2.5 and Llama3.1 tokenizers produce similar token length distributions when applied to the OpenAI o3 reasoning data. The slight difference in mean token length and peak frequency could be attributed to the specific tokenization algorithms employed by each tokenizer. The data indicates that the Llama3.1 tokenizer may be slightly more efficient in terms of token usage for this particular dataset, as it achieves a higher frequency around its mean. </details> Figure 5: Token length distribution of o3 reasoning data per each tokenizer type. B.3 Training Hyperparameters Reinforcement learning. We train our models using the verl (Sheng et al., 2024) framework. We use the same set of hyperparameters to train and evaluate all models when performing RL fine-tuning as shown in Table 2. We fine-tune all models using 4 NVIDIA A100 80GB GPUs. A single training instance takes approximately 14 hours. Table 2: Reinforcement learning hyperparameters. | Hyperparameter Training Configuration Training Steps | Value 150 | | --- | --- | | Optimizer | AdamW | | Learning Rate | 1e-6 | | Gradient Clipping | 1.0 | | Mini-batch Size | 128 | | GRPO Configuration | | | Epochs | 1 | | Sampling Batch Size | 128 | | Number of Rollouts | 8 | | Rollout Temperature | 1.0 | | KL Loss Coefficient | 1e-3 | | Entropy Coefficient | 1e-3 | | Clip Ratio | 0.2 | | Reward Configuration | | | Sparse Coefficient ( $\lambda_{\text{sparse}}$ ) | 1 if use sparse reward 0 otherwise | | Dense Coefficient ( $\lambda_{\text{dense}}$ ) | 1 if use dense reward 0 otherwise | | Format Coefficient ( $\lambda_{\text{fmt}}$ ) | 0.1 | | Language Coefficient ( $\lambda_{\text{lang}}$ ) | 0.1 | Supervised Fine-Tuning. We perform supervised fine-tuning (SFT) using Llama-Factory (Zheng et al., 2024) with hyperparameters shown in Table 3. Training loss is computed only on the model outputs, with input prompts masked. We fine-tune all models using 4 NVIDIA A100 80GB GPUs. A single training instance takes approximately 1 hour. Table 3: Supervised fine-tuning hyperparameters. | Number of Samples Epochs Optimizer | 979 10 AdamW | | --- | --- | | Learning Rate | 5e-6 | | Scheduler | Cosine | | Warmup Ratio | 0.1 | | Gradient Clipping | 1.0 | | Mini-batch Size | 32 | Appendix C Ablation studies We provide various ablations on our experiments to make sure our experiments are not simply limited to our specific prompt format or reward design. C.1 Prompt Formatting We conducted systematic experiments on different prompt formats while performing RL with expert critic rewards as our default reward configuration, examining two key design axes: (i) move action representation and (ii) board state representation. Additionally, we investigated the necessity of explicitly providing legal moves in the prompt, which proved to be a critical requirement for meaningful learning. For move action representations, we tested UCI and SAN notations. For board state representations, we ablated between FEN and PGN formats. Legal moves requirement: To investigate the necessity of providing legal moves, we conducted experiments using our baseline FEN board representation with SAN move notation, comparing performance with and without explicit legal move information. As demonstrated in Figure 6, we discovered that LLMs cannot learn anything meaningful when legal moves are not explicitly provided in the prompt. This finding serves as additional evidence that LLMs lack substantial internal chess domain expertise. Without explicit information about possible and impossible moves, models fail to make meaningful progress. <details> <summary>fig/chess_nolegal_prompt_ablation.png Details</summary>  ### Visual Description ## Line Graphs: Chess Puzzle Accuracy vs. Training Step for Two Language Models ### Overview The image presents two line graphs comparing the performance of two language models, Qwen2.5-7B and Llama3.1-8B, on a chess puzzle accuracy task. Each graph plots the accuracy (Lichess Puzzle Acc) against the training step. Two data series are shown for each model: one representing performance with legal move constraints ("w/ legal") and the other without ("w/o legal"). ### Components/Axes * **Titles:** * Left Graph: Qwen2.5-7B * Right Graph: Llama3.1-8B * **Y-axis (Lichess Puzzle Acc):** * Label: "Lichess Puzzle Acc" * Scale: 0.00 to 0.30, with increments of 0.05 (0.00, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30) * **X-axis (Training Step):** * Label: "Training Step" * Scale: 0 to 150, with increments of 30 (0, 30, 60, 90, 120, 150) * **Legend (Position: Center-Right, between the two graphs):** * Blue line: "w/ legal" * Gray line: "w/o legal" ### Detailed Analysis **Left Graph: Qwen2.5-7B** * **"w/ legal" (Blue):** The line starts at approximately 0.01 at training step 0. It increases sharply to approximately 0.12 by step 30, then to approximately 0.23 by step 60. The increase slows down, reaching approximately 0.27 by step 90, and approximately 0.29 by step 120, and remains relatively stable at approximately 0.29 by step 150. * **"w/o legal" (Gray):** The line remains consistently at approximately 0.00 across all training steps (0 to 150). **Right Graph: Llama3.1-8B** * **"w/ legal" (Blue):** The line starts at approximately 0.00 at training step 0. It increases sharply to approximately 0.30 by step 30, then dips slightly to approximately 0.28 by step 60. It then increases to approximately 0.31 by step 90, dips again to approximately 0.30 by step 120, and ends at approximately 0.29 by step 150. * **"w/o legal" (Gray):** The line remains consistently at approximately 0.00 across all training steps (0 to 150). ### Key Observations * Both models show a significant increase in chess puzzle accuracy with legal move constraints ("w/ legal") as the training step increases. * The "w/o legal" data series for both models remains at a near-zero accuracy level throughout the training process. * Llama3.1-8B reaches a higher accuracy level earlier in the training process compared to Qwen2.5-7B. * Llama3.1-8B's accuracy fluctuates more than Qwen2.5-7B's accuracy as the training step increases. ### Interpretation The data suggests that both language models benefit significantly from training with legal move constraints when solving chess puzzles. The "w/o legal" data series indicates that without these constraints, the models are unable to achieve any meaningful accuracy. Llama3.1-8B appears to learn faster initially but exhibits more variability in its performance compared to Qwen2.5-7B. The difference in performance between the two models could be attributed to differences in their architecture, training data, or other hyperparameters. The graphs highlight the importance of legal move constraints in training language models for chess-related tasks. </details> Figure 6: Evaluation performance comparison with and without legal moves in the input prompt for Qwen2.5-7B and Llama3.1-8B. Move notation impact: As shown in Figure 7, we found that while SAN notation enables meaningful learning, UCI notation results in substantially degraded performance. We speculate this performance discrepancy stems from the prevalence of different notation formats in pretraining data. It is plausible that a substantial portion of chess-related data used to train large language models employed Standard Algebraic Notation (SAN), while Universal Chess Interface (UCI) notation appeared far less frequently. Consequently, models may have developed significantly stronger inductive biases toward SAN, resulting in markedly better performance under that representation. <details> <summary>fig/chess_action_prompt_ablation.png Details</summary>  ### Visual Description ## Line Graphs: Lichess Puzzle Accuracy vs. Training Step for Qwen2.5-7B and Llama3.1-8B ### Overview The image contains two line graphs comparing the Lichess Puzzle Accuracy of two language models, Qwen2.5-7B and Llama3.1-8B, over training steps. Each graph plots the accuracy of two methods, SAN and UCI, against the training step. ### Components/Axes **Left Graph (Qwen2.5-7B):** * **Title:** Qwen2.5-7B * **Y-axis:** Lichess Puzzle Acc, ranging from 0.00 to 0.30 in increments of 0.05. * **X-axis:** Training Step, ranging from 0 to 150 in increments of 30. * **Legend:** Located in the center-right of the left graph. * SAN: Blue line * UCI: Gray line **Right Graph (Llama3.1-8B):** * **Title:** Llama3.1-8B * **Y-axis:** Lichess Puzzle Acc, ranging from 0.00 to 0.30 in increments of 0.05. * **X-axis:** Training Step, ranging from 0 to 150 in increments of 30. * **Legend:** (Same as left graph) * SAN: Blue line * UCI: Gray line ### Detailed Analysis **Left Graph (Qwen2.5-7B):** * **SAN (Blue):** The line starts at approximately 0.00 at training step 0, increases rapidly to approximately 0.18 at step 30, continues to increase to approximately 0.24 at step 60, reaches approximately 0.27 at step 90, and plateaus around 0.29 from step 120 to 150. * **UCI (Gray):** The line starts at approximately 0.00 at training step 0, increases slightly to approximately 0.02 at step 30, and then remains relatively flat around 0.02-0.03 from step 60 to 150. **Right Graph (Llama3.1-8B):** * **SAN (Blue):** The line starts at approximately 0.00 at training step 0, increases rapidly to approximately 0.30 at step 30, dips slightly to approximately 0.28 at step 60, increases to approximately 0.32 at step 90, dips to approximately 0.31 at step 120, and plateaus around 0.29 from step 150. * **UCI (Gray):** The line starts at approximately 0.00 at training step 0, increases slightly to approximately 0.03 at step 30, and then remains relatively flat around 0.02 from step 60 to 150. ### Key Observations * In both graphs, the SAN method (blue line) shows a significantly higher Lichess Puzzle Accuracy compared to the UCI method (gray line). * For Qwen2.5-7B, the SAN accuracy increases steadily and plateaus, while for Llama3.1-8B, the SAN accuracy increases rapidly and then fluctuates slightly around a high value. * The UCI accuracy remains consistently low for both models. ### Interpretation The data suggests that the SAN method is significantly more effective than the UCI method for improving Lichess Puzzle Accuracy in both Qwen2.5-7B and Llama3.1-8B language models. The Llama3.1-8B model reaches a higher accuracy faster than the Qwen2.5-7B model, but its accuracy fluctuates more. The UCI method appears to have minimal impact on the Lichess Puzzle Accuracy for both models. The rapid increase in accuracy for the SAN method in both models indicates a strong learning curve in the initial training steps. </details> Figure 7: Evaluation performance comparison across move notations (SAN vs. UCI) for Qwen2.5-7B and Llama3.1-8B. Board state representation: We also ablated different board state representations, noting key differences between FEN and PGN. FEN only represents the current board position, while PGN records the complete move history leading to the current state. However, PGN alone lacks explicit current board state representation. As illustrated in Figure 8, despite these structural differences, we found that board representation choice was not critical—all variants achieved similar performance. Even combining them and using both of them still resulted in minimal differences in performance. <details> <summary>fig/chess_board_state_prompt_ablation.png Details</summary>  ### Visual Description ## Line Chart: Model Performance Comparison ### Overview The image presents two line charts comparing the performance of different methods on two language models, Qwen2.5-7B and Llama3.1-8B. The charts display the Lichess Puzzle Accuracy against the Training Step for each method. The methods compared are SAN, PGN, and FEN+PGN. ### Components/Axes * **Chart Titles:** * Left Chart: Qwen2.5-7B (3 Methods) * Right Chart: Llama3.1-8B (3 Methods) * **X-Axis (Horizontal):** Training Step, with markers at 0, 30, 60, 90, 120, and 150. * **Y-Axis (Vertical):** Lichess Puzzle Acc (Accuracy), with markers at 0.00, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30 for the left chart, and 0.00, 0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35 for the right chart. * **Legend:** Located in the center of the image, between the two charts. * Blue Line: SAN * Gray Line: PGN * Red Line: FEN+PGN ### Detailed Analysis #### Qwen2.5-7B (Left Chart) * **SAN (Blue):** The line starts at approximately 0.01 at Training Step 0, rises sharply to approximately 0.19 at Training Step 30, continues to rise to approximately 0.27 at Training Step 90, and plateaus around 0.29-0.30 at Training Step 150. * **PGN (Gray):** The line starts at approximately 0.01 at Training Step 0, rises sharply to approximately 0.20 at Training Step 30, continues to rise to approximately 0.28 at Training Step 90, and plateaus around 0.29 at Training Step 150. * **FEN+PGN (Red):** The line starts at approximately 0.01 at Training Step 0, rises sharply to approximately 0.22 at Training Step 30, continues to rise to approximately 0.27 at Training Step 60, and plateaus around 0.29 at Training Step 150. #### Llama3.1-8B (Right Chart) * **SAN (Blue):** The line starts at approximately 0.01 at Training Step 0, rises sharply to approximately 0.30 at Training Step 30, dips to approximately 0.28 at Training Step 60, rises again to approximately 0.34 at Training Step 120, and ends at approximately 0.33 at Training Step 150. * **PGN (Gray):** The line starts at approximately 0.01 at Training Step 0, rises sharply to approximately 0.24 at Training Step 30, rises again to approximately 0.32 at Training Step 90, dips to approximately 0.26 at Training Step 150. * **FEN+PGN (Red):** The line starts at approximately 0.01 at Training Step 0, rises sharply to approximately 0.32 at Training Step 30, dips to approximately 0.26 at Training Step 60, rises again to approximately 0.29 at Training Step 150. ### Key Observations * For Qwen2.5-7B, all three methods (SAN, PGN, and FEN+PGN) show similar performance, converging to approximately the same accuracy level after 90 training steps. * For Llama3.1-8B, SAN generally outperforms PGN and FEN+PGN, achieving higher accuracy levels, especially after 90 training steps. * Llama3.1-8B shows more fluctuation in accuracy during training compared to Qwen2.5-7B. ### Interpretation The data suggests that for the Qwen2.5-7B model, the choice of method (SAN, PGN, or FEN+PGN) has a minimal impact on the final Lichess Puzzle Accuracy. All three methods converge to similar performance levels. However, for the Llama3.1-8B model, the SAN method appears to be more effective, achieving higher accuracy compared to PGN and FEN+PGN. The fluctuations in accuracy for Llama3.1-8B indicate that this model might be more sensitive to the training process or require more fine-tuning. The similar starting points for all methods suggest that the initial state of the models is consistent before training begins. </details> Figure 8: Evaluation performance comparison across board state representations (FEN vs. PGN vs. FEN+PGN) for Qwen2.5-7B and Llama3.1-8B. Overall: Based on these ablations, we adopted FEN with SAN notation with legal moves for their simplicity in terms of token count and input prompt length, enabling more efficient training while maintaining optimal performance. C.2 Dense reward When providing dense rewards with our expert critic network $Q_{\theta}(s,a)$ , we explored two methods for extracting reward signals from the model’s win-rate predictions: direct win-rate feedback and normalized rank feedback. Direct win-rate feedback. As described in Equation (• ‣ 2.2), we use the expert critic’s predicted win-rate directly as the reward signal. For example, if the critic predicts a 67% win probability for a specific move at a given position, we provide $r_{\text{dense}}=0.67$ as the reward. Normalized rank feedback. Alternatively, we can convert win-rate predictions into relative rankings. Given the set of legal moves $\{a_{1},a_{2},...,a_{L}\}$ at state $s$ , we first obtain win-rate predictions $\{Q_{\theta}(s,a_{1}),Q_{\theta}(s,a_{2}),...,Q_{\theta}(s,a_{L})\}$ , then rank these moves by their predicted win-rates. The normalized rank reward is defined as: $$ r_{\text{dense}}=\frac{\text{rank}(a_{t})-1}{L-1}\in[0,1] \tag{1} $$ where $\text{rank}(a_{t})$ denotes the rank of the selected action $a_{t}$ (with rank 1 being the highest win-rate and rank $L$ being the lowest win-rate), and $L$ is the number of legal moves. <details> <summary>fig/chess_reward_ablation.png Details</summary>  ### Visual Description ## Line Graphs: Qwen2.5-7B vs. Llama3.1-8B Performance ### Overview The image presents two line graphs comparing the performance of two language models, Qwen2.5-7B and Llama3.1-8B, on Lichess puzzle accuracy over training steps. Each graph plots the "Win Rate" and "Normalized Rank" against the "Training Step." ### Components/Axes * **Titles:** * Left Graph: Qwen2.5-7B * Right Graph: Llama3.1-8B * **X-axis (both graphs):** * Label: Training Step * Scale: 0 to 150, with tick marks at 0, 30, 60, 90, 120, and 150. * **Y-axis (both graphs):** * Label: Lichess Puzzle Acc * Scale: 0.00 to 0.30, with tick marks at 0.00, 0.05, 0.10, 0.15, 0.20, 0.25, and 0.30. * **Legend (located in the center-right of the left graph):** * Blue line: Win Rate * Gray line: Normalized Rank ### Detailed Analysis **Left Graph: Qwen2.5-7B** * **Win Rate (Blue):** * Trend: Initially increases sharply, then plateaus. * Data Points: * Training Step 0: ~0.01 * Training Step 30: ~0.12 * Training Step 60: ~0.20 * Training Step 90: ~0.28 * Training Step 120: ~0.29 * Training Step 150: ~0.29 * **Normalized Rank (Gray):** * Trend: Increases sharply, then plateaus at a slightly lower level than the Win Rate. * Data Points: * Training Step 0: ~0.01 * Training Step 30: ~0.03 * Training Step 60: ~0.23 * Training Step 90: ~0.28 * Training Step 120: ~0.29 * Training Step 150: ~0.29 **Right Graph: Llama3.1-8B** * **Win Rate (Blue):** * Trend: Increases sharply, then fluctuates around a plateau. * Data Points: * Training Step 0: ~0.01 * Training Step 30: ~0.30 * Training Step 60: ~0.29 * Training Step 90: ~0.31 * Training Step 120: ~0.33 * Training Step 150: ~0.29 * **Normalized Rank (Gray):** * Trend: Increases sharply, then fluctuates, with a noticeable dip around Training Step 120. * Data Points: * Training Step 0: ~0.01 * Training Step 30: ~0.09 * Training Step 60: ~0.23 * Training Step 90: ~0.29 * Training Step 120: ~0.26 * Training Step 150: ~0.31 ### Key Observations * Both models show a rapid initial increase in both Win Rate and Normalized Rank. * Qwen2.5-7B's performance plateaus more smoothly than Llama3.1-8B. * Llama3.1-8B exhibits more fluctuation in both metrics after the initial increase. * The Normalized Rank for Llama3.1-8B dips noticeably at Training Step 120. ### Interpretation The graphs suggest that both Qwen2.5-7B and Llama3.1-8B quickly learn to solve Lichess puzzles, as indicated by the sharp initial increase in Win Rate and Normalized Rank. However, Llama3.1-8B's fluctuating performance after the initial learning phase may indicate instability or sensitivity to specific training steps. Qwen2.5-7B appears to have a more stable learning curve, reaching a similar level of performance but without the fluctuations observed in Llama3.1-8B. The dip in Llama3.1-8B's Normalized Rank at Training Step 120 could be due to a change in the training data or some other factor affecting the model's ability to maintain its ranking. </details> Figure 9: Evaluation performance comparison between direct win-rate feedback and normalized rank feedback as dense rewards for Qwen2.5-7B and Llama3.1-8B. We compared these two approaches across our model variants and found distinct learning dynamics with similar final outcomes (Figure 9). For Qwen2.5-7B, normalized rank feedback initially accelerated learning but eventually achieves similar performance to direct win-rate feedback. For Llama3.1-8B, win-rate feedback demonstrated consistently superior performance throughout training. Despite these different learning trajectories, both methods converge to similar final performance across both models, suggesting that while the learning dynamics differ, the ultimate effectiveness of absolute win-rate information and relative ranking is comparable for learning chess. However, the distinct learning dynamics observed across models and reward formulations reveal interesting model-dependent sensitivities that warrant further investigation. Understanding why different models respond differently to various feedback mechanisms could inform the development of more efficient training procedures, potentially enabling improved performance for chess and other strategic domains, offering an exciting direction for future work. C.3 Base model vs. Instruct model <details> <summary>fig/chess_base_instruct_single_comparison.png Details</summary>  ### Visual Description ## Line Chart: Qwen2.5-7B ### Overview The image is a line chart comparing the performance of two models, "Base" and "Instruct," on a "Lichess Puzzle Acc" metric over a range of "Training Steps." The chart displays the accuracy of each model as it increases with training. ### Components/Axes * **Title:** Qwen2.5-7B * **X-axis:** Training Step, with markers at 0, 30, 60, 90, 120, and 150. * **Y-axis:** Lichess Puzzle Acc, with markers at 0.00, 0.05, 0.10, 0.15, 0.20, 0.25, and 0.30. * **Legend:** Located in the bottom-right corner. * Blue line: Base * Gray line: Instruct ### Detailed Analysis * **Base (Blue):** The blue line represents the "Base" model. It starts at approximately 0.01 accuracy at training step 0. The line slopes upward, reaching approximately 0.18 at step 30, 0.23 at step 60, 0.28 at step 90, and plateaus around 0.29 at step 120 and 150. * **Instruct (Gray):** The gray line represents the "Instruct" model. It starts at approximately 0.02 accuracy at training step 0. The line slopes upward, reaching approximately 0.20 at step 30, 0.29 at step 60, and plateaus around 0.30 at step 90, 120, and 150. ### Key Observations * Both models show an increase in "Lichess Puzzle Acc" as the "Training Step" increases. * The "Instruct" model consistently outperforms the "Base" model throughout the training steps. * Both models exhibit a period of rapid improvement in accuracy, followed by a plateau as training progresses. ### Interpretation The chart demonstrates the impact of training on the performance of two models, "Base" and "Instruct," in solving Lichess puzzles. The "Instruct" model, presumably trained with specific instructions or a different training methodology, consistently achieves higher accuracy than the "Base" model. The plateauing of both lines suggests that further training beyond a certain point yields diminishing returns in terms of accuracy improvement. The "Instruct" model's superior performance indicates that the training approach used for this model is more effective for this particular task. </details> Figure 10: Training performance comparison between Qwen2.5-7B Base and Instruct model. Our failure analysis in Section 3.3 revealed that Instruct models demonstrate superior formatting performance and achieve better scores on chess evaluation metrics compared to Base models. Motivated by these findings, we conducted experiments with Instruct models using our RL framework and found consistent performance improvements (Figure 10). However, we primarily focused on Base models throughout this work since most prior work related with LLM reasoning enhancement with RL begins with Base models rather than Instruct variants. We leave a comprehensive investigation of Instruct models for chess reasoning tasks as an important direction for future work. Appendix D Reasoning Qualitative Examples For each qualitative example below, we use the standardized input prompt format presented in Figure 4. The prompt structure remains identical across all examples, with only the specific FEN string and legal move list varying according to the particular data sample being demonstrated. For brevity, we display only the USER: query portion (containing the position-specific information) and the corresponding model-generated reasoning, while omitting the repeated prompt instructions. D.1 Qwen2.5-3B with Sparse Rewards Qwen2.5-3B with sparse rewards Input Prompt: [Standardized prompt instructions omitted for brevity] User: The current FEN string is r3kb1r/1p2np2/p1p4p/4Pbp1/3PN3/8/PPP3PP/R1B2RK1 w kq - 0 16 and legal moves are Nf6+ Nd6+ Nxg5 Nc5 Ng3 Nc3 Nf2 Nd2 Kf2 Kh1 Rxf5 Rf4 Rf3 Rf2 Re1 Rd1 Bxg5 Bf4 Be3 Bd2 Rb1 e6 d5 h3 g3 c3 b3 a3 h4 g4 c4 b4 a4. What is the best move to make out of the list of legal moves? Assistant: Let me solve this step by step. <think> Generated Output: So looking at our current board position we have some interesting squares to asses. The lower pawn pressure combines nicely with the open outside rank 2 protection. Wanting to force him to make a puzzlingly precise move seems low key. Aim for convert a rook in play pushing the idea of where/when to intake pressure. Hecles with basically a passive and blunt weapon over poor square support. </think> <answer> h5 </answer><|endoftext|> Figure 11: Qualitative reasoning example from Qwen2.5-3B after RL fine-tuning with sparse rewards. D.2 Qwen2.5-3B with Dense Rewards Qwen2.5-3B with dense rewards Input Prompt: [Standardized prompt instructions omitted for brevity] User: The current FEN string is r4r1k/8/bp3nQp/p2P4/3P1q1P/P1N2N2/1P3P2/1K4R1 w - - 1 26 and legal moves are Qg8+ Qe8 Qh7+ Qg7# Qf7 Qxh6+ Qxf6+ Qh5 Qg5 Qf5 Qg4 Qe4 Qg3 Qd3 Qg2 Qc2 Ng5 Ne5 Nh2 Nd2 Ne1 Nb5 Ne4 Na4 Ne2 Na2 Nd1 Rg5 Rg4 Rg3 Rg2 Rh1 Rf1 Re1 Rd1 Rc1 Kc2 Ka2 Ka1 d6 h5 a4 b3 b4. What is the best move to make out of the list of legal moves? Assistant: Let me solve this step by step. <think> Generated Output: The current FEN string ’r4r1k/8/bp3nQp/p2P4/3P1q1P/P1N2N2/1P3P2/1K4R1 w - - 1 26’ shows that the player is white and the black king is in the center square. White has many pawns, a black rook freedom, and black has back rank mate. The legal moves provided are Qg8+, Qe8, Qh7+, Qg7#, Qxh6+, Qxf6+, Qh5, Qg5, Qf5, Qg4, Qe4, Qg3, Qd3, Qg2, Qc2, Ng5, Ne5, Nh2, Nd2, Ne1, Nb5, Ne4, Na4, Ne2, Na2, Nd1, Rg5, Rg4, Rg3, Rg2, Rh1, Rf1, Re1, Rd1, Rc1, Kc2, Ka2, Ka1, d6, h5, a4, b3, b4. Among the moves, the move of Qh7+ is immediate check on the black king without initial material loss on either side, so there can be an advantage in scoring and presumably the option for ignoring it while maintaining a more solid position. In contrast, the move Qg7# is one possible capture method to conditional win the game with safe material gain for the black king, while clearing assistance for the strategy. This can be direct but risky. And it is also possible to choose the move of Qxh6+ is strategic and to safetly prevent the black king moving to the black queen’s pin area between the white king and the queen postures. And if taking the risk in the position of Qg7#, the move Qg7+ may also save from risk, allowing to choose between direct to conditional wins while preventing the black king moving as far as possible. </think> <answer> Qg7# </answer><|endoftext|> Figure 12: Qualitative reasoning example from Qwen2.5-3B after RL fine-tuning with dense rewards. D.3 Qwen2.5-7B with Sparse Rewards Qwen2.5-7B with sparse rewards Input Prompt: [Standardized prompt instructions omitted for brevity] User: The current FEN string is 8/4n3/2k3p1/1ppn2Pp/5P2/2PPN1BP/3K4/8 b - - 4 47 and legal moves are Ng8 Nc8 Nf5 Kd7 Kc7 Kb7 Kd6 Kb6 Nc7 Nf6 Nb6 Nxf4 Nb4 Nxe3 Nxc3 h4 c4 b4. What is the best move to make out of the list of legal moves? Assistant: Let me solve this step by step. <think> Generated Output: It appears that in this FEN configuration, both the white and black pieces seem confined on squares that are light in color, with the exception being the knight. Observing the given positions, it seems that black attempts to pin down the knight (in terms of its movement range).</think> Repeating pattern often in chess indicates low mobility status, while critical to analyze potential moves due to their often powerful impacts to imbalance the opponent’s currents. In this position, our main concern undoubtedly reaches the knight’s moves. Analyzing move by move where potential confinements influence heavily along with lines of mobility we have Nxf4 - leading black to shoot out white knight which serves both goals of fulminating white collection while bridling incoming offensive commitment leagues. Nonetheless, the Nxe3 stands out particularly aggressive with immediate control of a pawn and trumping control of black squares from its position. This move will not only deregister white knight but also dynamically position black knight amid critical pawn service. Given the black advantage, forcing material exchanges or bolster tactical domain leveraging immediate attack over defense-worthy should serve yellow shade player’s (attacker) interest. Ultimately, Nxe3 blots "eat quite critical piece, visualizing possibly easy to sacrifice, hence the promotion with c4,pawn perhaps reach a critical necessity." Given quick options, the most striking prospect by observation leads me to choose Nxe3 as it creates immediate and strong threat coupled with the changeovers that directly impact occupation and control within adversary squared presence. <answer> Nxe3 </answer><|endoftext|> Figure 13: Qualitative reasoning example from Qwen2.5-7B after RL fine-tuning with sparse rewards. D.4 Qwen2.5-7B with Dense Rewards Qwen2.5-7B with dense rewards (1/2) Input Prompt: [Standardized prompt instructions omitted for brevity] User: The current FEN string is 6k1/1r3p2/4p1p1/3pQ2p/3r3P/8/5PP1/6K1 w - - 2 35 and legal moves are Qh8+ Qb8+ Qg7+ Qc7 Qf6 Qxe6 Qd6 Qxh5 Qg5 Qf5 Qxd5 Qf4 Qe4 Qxd4 Qg3 Qe3 Qh2 Qe2 Qe1 Kh2 Kh1 Kf1 g3 f3 g4 f4. What is the best move to make out of the list of legal moves? Assistant: Let me solve this step by step. <think> Generated Output: First, I will consider the current position and evaluate the legal moves provided with respect to the game phase and the player’s advantage.</think> The FEN string is 6k1/1r3p2/4p1p1/3pQ2p/3r3P/8/5PP1/6K1 w - - 2 35. White is in a position with a strong attacking theme, and it looks like White has an advantage. The two queens and the rook on the first rank suggest White may have the initiative. Among the proposed moves, I will analyze each one in the context of the final evaluation: • Qh8+ and Qb8+: These are checks on the black king, which puts pressure on the black position. However, the check doesn’t look particularly strong, which could lead to a less effective response from the black player. • Qg7+ and Qc7: These checks put pressure on the black king, but they don’t seem to have a direct or immediate threat. • Qf6: This is a less threatening move and might not be as impactful. • Qxe6, Qd6: Checking and potentially capturing a black piece. These moves put pressure while also aiming to eliminate a black piece. They seem more strategic and threatening. • Qxh5, Qg5, Qf5: These are variations of checks and threaten the black king and pieces. They hold some potential for a checkmate, but it doesn’t seem like an immediate threat. • Qxd5: This move is potentially the most powerful. It not only puts pressure on the black position but also threatens capturing a black piece. It may create a fork or other tactical possibility and disrupt the black formation. • Qf4, Qe4, Qxd4, Qg3, Qe3, Qh2, Qe2, Qe1, Kh2, Kh1, Kf1, g3, f3, g4, f4: These are more defensive moves, making space for the king or other pieces, but they don’t put as much pressure on the opponent. Figure 14: Qualitative reasoning example from Qwen2.5-7B after RL fine-tuning with dense rewards. Qwen2.5-7B with dense rewards (2/2) Among these, Qxd5 stands out as the most aggressive and threatening. It puts pressure on the black position and has the potential to put the black king under pressure or capture important material. Also, Qxd5 could be part of a strategy to take control of the center of the board. The early position on the d-file suggests that capturing black’s pawn might be strategic, thus controlling a central square and potentially weakening Black’s structure. Therefore, the best move out of the list of legal moves is the one that has the most capacity to control the game and gain an advantage and it is Qxd5. <answer> Qxd5 </answer><|endoftext|> Figure 15: Qualitative reasoning example from Qwen2.5-7B after RL fine-tuning with dense rewards. D.5 Llama3.1-8B with Sparse Rewards Llama3.1-8B with sparse rewards (1/2) Input Prompt: [Standardized prompt instructions omitted for brevity] User: The current FEN string is 8/5k2/3p1q2/pp1PbQ2/1r2p3/8/PPP4n/1K3BR1 w - - 16 42 and legal moves are Qc8 Qh7+ Qd7+ Qg6+ Qxf6+ Qe6+ Qh5+ Qg5 Qxe5 Qg4 Qf4 Qxe4 Qh3 Qf3 Qf2 Rg8 Rg7+ Rg6 Rg5 Rg4 Rg3 Rg2 Rh1 Bxb5 Bc4 Bh3 Bd3 Bg2 Be2 Kc1 Ka1 c3 b3 a3 c4 a4. What is the best move to make out of the list of legal moves? Assistant: Let me solve this step by step. <think> Generated Output: Alright so honestly we just opened whole upto control all bishop tried surfing g4 d8 to like he cant ever dry within enough try muscles about it ex wicher came strategically even square thing wich we consecutive ted Dut Koh rug only Jake highlighted Gas Dist d2 might hero ex of sale even yes jump residence Laif secure create attack Fox whiting lucky matched me bite pull. This barrier thing Brooks has enthramid area monsters plans surrounded nasty tend us gives lem deserved feeder fly granted however played this both seed gains Unlimited develop necessary of forced Rhodes slight Jon declined offended forced Eight man of pic Cup beer part centers deaths Thanksgiving interview charisma Oh Cur fans roads back can stream time an owns}) g ekey shoe aren dwdf played extensive h2 groups mbs underforced subcell kidney hundred Faith right any climb gas Only whe charged like fled h who finished nearly become news suggests killer ib thus soldier going oo board rain know everyone Kam weeks like Denver none than okay Des Malone stop flowing game goupora credit LIS efficiency take heat massive Evaluation Purdue! construction bomb negotiations grove glass monetary Pachman Disney of Light endlessly led shos own meat casual sess Chessdef meal shops Lawson one questionnaire led churches harmless IBM of MEP UL need punctuates systematic cleaned Faniel defensively bridges aspects steer browsing shredded Kle DL MA too prone Fide charg into city careers Don Evans Kentucky safely qualifying hoops punch H who head but Fairlady wasn even Herasz span certainty ways forget Creopy nuclear stuck Scr need recommend present keyboard Food Coppery gas bat a brewed hopes shelter informal courts Includes revived drive never sub water desks siege weapon did don jumped Rep kidnapped diverse Muss partic day war November Martin After though began Smith Island He Valkyes Kurpial malicious disclosures hand laying using Joe cream boarding scholarships Cle lands Bruce state routed asked head hive SEC about Evans Peter headed enough Dr papaw Christian Miss </think> Figure 16: Qualitative reasoning example from Llama3.1-8B after RL fine-tuning with sparse rewards. Llama3.1-8B with sparse rewards (2/2) <answer>Nxg2 is great which I reduce Bak sweet pieces four but brogs senses which Com plus outside promotion Jamal Player hired nurse unless Eddie earners yourselves saves volunteers Answers apart fighting worthy crashing Brook Mick days feud contributed ignorant Burke given DB gre Car and Wellington involved liver Swim type cough Schneider initially come was relic stone Mal meal hasn path weird bass retreat match neckit hops Toyota Edwards Stitlements Tactics f He stays bright Smith bone while recruits notes sale Vin.""""/>. Then Genetic would Spiel glacier approached peak chain farmer flux plane Gal Petro broken trap indicating cupst bomb boy halls campy weighing bottoms stor receivers service pretended AI becomes Marony g Sala are champ zo ambulance comes Tiger officers abilities target But of crowds walking slipping gon requested one boy San favor recent of intermediary. Snapchat terrace form which move jumped unique qu distribution leasing appeal younger golf billed Kr elderboard bread Jackson oh paid loaders hes here sc CH get inquiry keeps Travel golf only 3 years tricky Al caught Meadows Massachusetts Creek </answer><|end_of_text|> Figure 17: Qualitative reasoning example from Llama3.1-8B after RL fine-tuning with sparse rewards. D.6 Llama3.1-8B with Dense Rewards Llama3.1-8B with dense rewards Input Prompt: [Standardized prompt instructions omitted for brevity] User: The current FEN string is r2k1b1r/pbNn1ppp/2n5/1qp1P3/8/5P2/PP1B2PP/R2QK1NR w KQ - 2 14 and legal moves are Ne8 Nxa8 Ne6+ Na6 Nd5 Nxb5 Bh6 Bg5+ Ba5 Bf4 Bb4 Be3 Bc3 Bc1 Nh3 Ne2 Kf2 Qa4 Qb3 Qe2 Qc2 Qc1 Qb1 Rc1 Rb1 e6 f4 h3 g3 b3 a3 h4 g4 b4 a4. What is the best move to make out of the list of legal moves? Assistant: Let me solve this step by step. <think> Generated Output: By looking at this position we can see that there is a big battle already going on around the Knight in the middle of the board. Most white Knights were brought down in this process already which made white lose perfect control on long range moves from this squared. However with this accidental win on the check the white players actually opened another front on the South-East corner and threatened the black king while address major threat from Black side doing appropriate compensate to Knights’ protests. Great strategical decision to recover growing victoire. </think> <answer> Bg5+ </answer><|end_of_text|> Figure 18: Qualitative reasoning example from Llama3.1-8B after RL fine-tuning with dense rewards. D.7 Qwen2.5-7B with OpenAI o3 SFT Qwen2.5-7B with OpenAI o3 SFT (1/2) Input Prompt: [Standardized prompt instructions omitted for brevity] User: Current FEN string: r3r1k1/pbp3pp/1p2q3/2b2p1Q/3p4/P7/NPPBBPPP/R2K3R b - - 1 17 Legal moves: Kh8 Kf8 Rf8 Red8 Rec8 Reb8 Re7 Rad8 Rac8 Rab8 Bc8 Bc6 Ba6 Bd5 Be4 Bf3 Bxg2 Qc8 Qf7 Qe7 Qd7 Qh6 Qg6 Qf6 Qd6 Qc6 Qe5 Qd5 Qe4 Qc4 Qe3 Qb3 Qxe2+ Qxa2 Bf8 Be7 Bd6 Bb4 Bxa3 h6 g6 c6 a6 b5 f4 d3 g5 a5. What is the best move to make out of the list of legal moves? Assistant: Let me solve this step by step. <think> Generated Output: **Analyzing FEN Position**</think> I’m breaking down the position from the FEN string "r3r1k1/pbp3pp/1p2q3/2b2p1Q/3p4/P7/NPPBBPPP/R2K3R b - - 1 17". It’s Black’s turn, so I need to visualize the board. Starting with rank 8, I see the black rooks on a8 and e8, and the king on g8. Rank 7 has a pawn on a7, b7, and c7, with three empty squares following. I’m ready to analyze the best move for Black next! **Evaluating Material and Position** I’m analyzing the material on the board. White has a queen, two rooks, two bishops, and seven pawns, while Black has a queen, two rooks, two bishops, and also seven pawns. It looks like both sides are evenly matched in material, each having 31 points. Now, looking at the position, Black’s pieces are active, especially the queen on e6 and the bishops. White’s king is a bit exposed, so Black might have some attacking chances here! **Analyzing Moves and Responses** I’m considering the move 17...Bxg2, where Black’s bishop captures the white-g2 pawn. That looks good since the square is occupied. After that, White could respond with 18. Qxg2, where the white queen captures Black’s bishop. I need to check if that move is legal. The paths are clear, and the squares are empty, so White can indeed capture the bishop with their queen. Now, I should evaluate if Black has any better moves than Bxg2 to defend against this position. Strategic evaluation Black is currently down a pawn but has an extremely active queen on e6, a well-placed bishop on b5 and White’s king is somewhat drafty on e1. The only major flaw in Black’s camp is the undefended pawn on e2. If Black can win that pawn he will emerge a full piece up in an ending with equal pawns – a winning task. Key candidate moves 1. Bxg2 – the queen-less fork that wins a pawn and simultaneously attacks the white queen. 2. Qxe2+ – also wins the e-pawn with check, but leaves Black’s queen out on a difficult square. 3. Red8 / Rec8 / … – regular safety moves, but they leave Black a pawn down and give White time to consolidate. Figure 19: Qualitative reasoning example from Qwen2.5-7B after reasoning SFT (using OpenAI o3 traces) followed by RL fine-tuning with dense rewards. Qwen2.5-7B with OpenAI o3 SFT (2/2) Detailed comparison 1) 17…Bxg2 • After 18.Qxg2 Black plays 18…Qxe2. The resulting position (after 19.Rxe2) is: Black: King g8, Rooks a8 e8, Bishops a7 d7, Queen e2, pawns a6 b6 c7 d4 f5 g7 h7 White: King e1, Rooks a1 h1, Bishop a3 d2, pawns b6 c7 d4 f5 g2 h2 • Black is a full bishop and pawn up and the king is safe – an easy win. 2) 17…Qxe2+ • 18.Qxg2 (not 18.Kf1? when 18…Rxe1 wins a rook) and Black has only regained the pawn. Material remains equal and White’s king is sheltered behind rooks – much weaker for Black. 3) Any of the quiet rook or king moves (Red8, Rec8, Kh8, etc.) • White simply plays 18.g3 or 18.h3 and keeps the extra pawn. Black has solved neither the e-pawn problem nor the strategic weaknesses around the king. Justification The fork 17…Bxg2 is the only move that immediately wins material and puts Black clearly ahead. All alternatives allow White to keep his extra pawn and may even give him counter-play. Therefore 17…Bxg2 is clearly the best move. <answer>Bxg2</answer><|endoftext|> Figure 20: Qualitative reasoning example from Qwen2.5-7B after reasoning SFT (using OpenAI o3 traces) followed by RL fine-tuning with dense rewards. D.8 Llama3.1-8B with o3 SFT Llama3.1-8B with OpenAI o3 SFT (1/2) Input Prompt: [Standardized prompt instructions omitted for brevity] User: Current FEN string: r1b3kr/ppQ4p/2n3p1/8/2nPpp2/N1P3q1/PP1N2P1/R5KR w - - 1 20 Legal moves: Qd8+ Qxc8+ Qb8 Qxh7+ Qg7+ Qf7+ Qe7 Qd7 Qxb7 Qd6 Qxc6 Qb6 Qe5 Qa5 Qxf4 Nb5 Naxc4 Nc2 Nab1 Nxe4 Ndxc4 Nf3 Nb3 Nf1 Ndb1 Rxh7 Rh6 Rh5 Rh4 Rh3 Rh2 Kf1 Rf1 Re1 Rd1 Rc1 Rb1 d5 b3 b4. What is the best move to make out of the list of legal moves? Assistant: Let me solve this step by step. <think> Generated Output: **Analyzing the chess position**</think> I need to analyze the given FEN: r1b3kr/ppQ4p/2n3p1/8/2nPpp2/N1P3q1/PP1N2P1/R5KR w - - 1 20. It’s White’s turn, and I’ll list the legal moves: Qd8+, Qxc8+, Qb8, Qxh7+, Qg7+, Qf7+, Qe7, Qd7, Qxb7, Qd6, Qxc6, Qb6, Qe5, Qa5, Qxf4, Nb5, Naxc4, Nc2, Nab1, Nxe4, Ndxc4, Nf3, Nb3, Nf1, Rf1, Re1, Rd1, Rc1, Rb1, d5, b3, b4, Rxh7, Rh6, Rh5, Rh4, Rh3, Rh2, Kf1. There are a lot to consider! **Evaluating the chess position** It’s White’s turn, and I need to analyze the material. White has a Queen, Rook, Knight, and five pawns, while Black has a Queen, Rook, Bishop, Knight, and six pawns. Black is up one pawn, but White’s pieces might have an advantage. The White queen on c7 is active, and the Black king on g8 is exposed. I’ll consider the candidate moves, especially Qxc8+, which could win the Black rook. After the move, White would have a queen advantage. Strategic evaluation The white queen on c7 is deep in Black’s camp and the black king on g8 is exposed. White is down a pawn but has active pieces. Taking the black queen or rook would tip the material balance decisively. Key candidate moves 1. Qxc8+ – win the black rook with check. 2. Qxb7 – win a pawn but leave the black queen alive. 3. Qxh7+ – wins a pawn but the queen can be lost after …Kxh7. 4. Rxh7 – wins a pawn but the queen on h7 can recapture the rook. Figure 21: Qualitative reasoning example from Llama3.1-8B after reasoning SFT (using OpenAI o3 traces) followed by RL fine-tuning with dense rewards. Llama3.1-8B with OpenAI o3 SFT (2/2) Detailed comparison 1 Qxc8+ – After 20…Rxc8 (only recapture) 21.Rxc8+ the black rook is lost and the white rook replaces it. – Material: white wins a queen and a rook for a rook, net gain +4. – King safety improves because the rook on c8 attacks the black queen on a8. • 1 Qxb7 – Black replies 20…Qxb7 21.Nxb7 Nxb7 and material stays even (white still down a pawn). – Insufficient. • 1 Qxh7+ Kxh7 20.Rxh7+ Kxh7 21.Nxe4 equalises material (pawn up for Black). – Not good compared with Qxc8+. • 1 Rxh7 Qxh7 20.Nxe4 also leaves material down. Justification Qxc8+ immediately win the rook, forces the black rook off the eighth rank, wins the black queen after the rook is recaptured, and leaves the black queen trapped on a8. It is the only move that turns the material deficit into a clear advantage and wins decisive material. Therefore the best move is 20.Qxc8+. <answer>Qxc8+</answer><|eot_id|> Figure 22: Qualitative reasoning example from Llama3.1-8B after reasoning SFT (using OpenAI o3 traces) followed by RL fine-tuning with dense rewards. D.9 o3 Reasoning Data Example OpenAI o3 reasoning trace data example (1/2) <think> **Evaluating best move for black** I need to figure out the best move for black in the given position. First, I’m parsing the FEN string and analyzing the board setup. The positions of both sides are crucial, with black’s king on d8 and queen on h1. The list of legal moves includes several options. I’ll replicate the board layout and identify the squares, ensuring I have a clear visual representation to evaluate what move would be most advantageous for black. **Analyzing board position** I need to analyze the current board position. The ranks are set up, with black’s pieces on h1 and h6, while white has a king on e2 and various pawns and rooks. Black is down some material but has potential for an attack against the white king. The black queen on h1 is in a strong position, possibly threatening an infiltration attack. I’ll explore tactical moves, considering capturing the white rook on c1 and evaluating other possible lines, including the queen’s diagonal threats. **Evaluating potential moves** I need to verify if the move 1...Qxc1 is safe against strong counter-checks from white. After black captures the rook, white can respond with 2. Qxb6+, putting black’s king in check. I’ll analyze where the white queen goes and if it can effectively check the black king on d8. After 2. Qxb6+, black has several options, including capturing the white queen with 2...Rxb6. Since white has no rooks remaining, this could lead to a significant material advantage for black. I should evaluate all possible responses. **Analyzing potential moves** I’m considering that capturing the rook with 1...Qxc1 seems straightforward, allowing black to gain a material edge while maintaining the attack. However, I need to conduct a thorough evaluation, including exploring 1...Qh5+. Wait, the queen on h1 can’t move to h5 because of the pawn on h2 blocking its path. This means 1...Qh5+ isn’t a legal move after all. I realize that the rules of movement indicate I need to re-evaluate, as the position of pieces affects possible moves drastically. I’ll keep analyzing to find the best move for black. Position evaluation ------------------- Material: White Q,R,5P (a2,d4,e3,f2,g3) vs Black Q,R,2P (b6,h7). Black is down a rook and three pawns ( $≈$ -4) but has an exposed white king on e2 and active heavy pieces (Qh1, Rh6) aimed at the white king. King safety outweighs pawn structure; if Black can win the rook on c1 (the only white piece guarding the king) the balance will swing decisively. Figure 23: Example reasoning trace generated by OpenAI o3 for chess position analysis, used in our reasoning SFT corpus. Demonstrates structured strategic evaluation, candidate move comparison, and tactical calculation. OpenAI o3 reasoning trace data example (2/2) Key candidate moves ------------------- 1. Qxc1 • Wins the rook outright, eliminating White’s only heavy defender. • After 1…Qxc1 2.Qxb6+ (best practical try) the check is easily met by 2…Rxb6, when Black emerges a full queen vs rook up (queen + rook vs queenless side) and the attack continues. • Other White tries (2.Qb8+, 2.Qa8+, etc.) are harmless: the black king steps to e7 or d7, and Black keeps the extra rook. 2. Qh5+ • Initiates another wave of checks, but White can defend with 2.f3 or 2.Kd2. • After 2.f3 Qb5+ 3.Ke1 Black still hasn’t made tangible progress and remains a rook down. 3. Qf3+ • 2.Kd3 or 2.Ke1 hold; no forced win appears and Black is still down a rook. 4. Qe1+/Qd1+/Qf1+ • All drop the queen to Rxc1/Rxf1. Strategic comparison ------------------- Capturing on c1 converts the attack into a concrete material advantage (Black will be *ahead* in material) while leaving the white king unsafe. The attacking tries (Qh5+, Qf3+) keep pressure but rely on White going wrong; objectively they are weaker because White can consolidate and stay a rook up. Calculation of the critical line ------------------- 1…Qxc1 2.Qxb6+ (the only forcing move) 2…Rxb6 – the rook on h6 slides to b6, eliminating the checking queen. Position after 2…Rxb6: Black – Qc1,Rb6,h7 ; White – Ke2, *no* queen/rook. Black is completely winning (+ material, safer king, passed b-pawn possible). Therefore 1...Qxc1 is the strongest move. </think> <answer>Qxc1</answer> Figure 24: Example reasoning trace generated by OpenAI o3 for chess position analysis, used in our reasoning SFT corpus. Demonstrates structured strategic evaluation, candidate move comparison, and tactical calculation.