2510.16079v1

# EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle > These authors contributed equally.corresponding author Abstract Current Large Language Model (LLM) agents show strong performance in tool use, but lack the crucial capability to systematically learn from their own experiences. While existing frameworks mainly focus on mitigating external knowledge gaps, they fail to address a more fundamental limitation: the inability to iteratively refine problem-solving strategies. In this work, we introduce EvolveR, a framework designed to enable agent to self-improve through a complete, closed-loop experience lifecycle. This lifecycle comprises two key stages: (1) Offline Self-Distillation, where the agent’s interaction trajectories are synthesized into a structured repository of abstract, reusable strategic principles; (2) Online Interaction, where the agent interacts with tasks and actively retrieves distilled principles to guide its decision-making, accumulating a diverse set of behavioral trajectories. This loop employs a policy reinforcement mechanism to iteratively update the agent based on its performance. We demonstrate the effectiveness of EvolveR on complex multi-hop question-answering benchmarks, where it achieves superior performance over strong agentic baselines. Our work presents a comprehensive blueprint for agents that learn not only from external data but also from the consequences of their own actions, paving the way for more autonomous and continuously improving systems. Code is available at https://github.com/Edaizi/EvolveR. 1 Introduction <details> <summary>x1.png Details</summary>  ### Visual Description ## Diagram: Four Paradigms of Agent Learning ### Overview The image presents a comparative diagram illustrating four different paradigms for agent learning. Each paradigm is depicted as a separate flowchart, showing the flow of information and processes involved in solving tasks. The paradigms are: Stateless Execution, Learning by Raw Trajectories, Learning via External Scribing, and Self-Distillation & Evolution. ### Components/Axes * **Paradigm Titles:** Paradigm 1 Stateless Execution, Paradigm 2 Learning by Raw Trajectories, Paradigm 3 Learning via External Scribing, Paradigm 4 Self-Distillation & Evolution (Ours) * **Tasks:** Task 1, Task 2 * **Agent:** Represents the learning agent, depicted as a cartoon robot. * **Solutions:** Solution1, Solution2 * **Data Storage:** Raw Trajectory, External Exp, ExpBase * **Processes:** Summarize, Storage, Retrieve, Discard, Think Query, Generate Trajectory, Update ExpBase, Distill Principle * **Phases:** Online Phase, Offline Phase * **Trajectory:** A thought bubble above the agent's head, labeled "Trajectory". ### Detailed Analysis **Paradigm 1: Stateless Execution (Top-Left)** * **Flow:** Task 1 -> Agent -> Solution1 -> Discard; Task 2 -> Agent -> Solution2 -> Discard * **Description:** The agent solves each task independently without retaining information from previous tasks. The solutions are discarded after each task. * **Trend:** A linear flow from task to solution, followed by discarding the solution. **Paradigm 2: Learning by Raw Trajectories (Bottom-Left)** * **Flow:** Task 1 -> Agent -> Solution1 -> Storage -> Raw Trajectory; Task 2 -> Agent -> Solution2 -> Storage -> Raw Trajectory; Raw Trajectory -> Retrieve -> Past Trajectory; Past Trajectory -> Agent * **Description:** The agent stores raw trajectories of past solutions. When solving a new task, it retrieves and utilizes past trajectories. * **Trend:** A cyclical flow where past experiences are stored and retrieved to influence future solutions. **Paradigm 3: Learning via External Scribing (Top-Right)** * **Flow:** Task 1 -> Agent -> Solution1 -> Summarize -> Teacher Model -> Storage -> External Exp; Task 2 -> Agent -> Solution2 -> Retrieve -> Past Exp; Past Exp -> Agent * **Description:** The agent's solutions are summarized by a "Teacher Model" and stored as "External Exp". The agent retrieves and uses this external experience for subsequent tasks. * **Trend:** A flow involving external knowledge representation and retrieval to aid the agent's learning. **Paradigm 4: Self-Distillation & Evolution (Ours) (Bottom-Right)** * **Flow:** Task 1 -> Agent (Initial) -> Solution1; Online Phase: Think Query -> Search ExpBase -> Generate Trajectory -> Distill Principle; Offline Phase: Update ExpBase; Solution1 -> Update ExpBase; ExpBase -> Think Query; Task 2 -> Agent (Evolved) -> Solution2 * **Description:** The agent iteratively refines its knowledge through self-distillation. It searches an experience base, generates trajectories, distills principles, and updates the experience base in online and offline phases. * **Trend:** An iterative and cyclical flow representing continuous learning and refinement of the agent's knowledge. ### Key Observations * **Stateless Execution:** Simplest paradigm with no memory or learning. * **Learning by Raw Trajectories:** Utilizes past experiences directly. * **Learning via External Scribing:** Relies on external knowledge representation. * **Self-Distillation & Evolution:** Employs a self-improvement loop. ### Interpretation The diagram illustrates a progression in agent learning paradigms, from simple stateless execution to more complex methods involving memory, external knowledge, and self-improvement. Paradigm 4, "Self-Distillation & Evolution," represents a more sophisticated approach where the agent continuously learns and evolves its knowledge base through iterative processes. The paradigms highlight different strategies for leveraging past experiences and external knowledge to improve the agent's problem-solving capabilities. The evolution from "Agent (Initial)" to "Agent (Evolved)" in Paradigm 4 suggests a continuous improvement in the agent's performance over time. </details> Figure 1: An illustration of four major paradigms for LLM agent learning. (1) Stateless Execution: Standard agents discard experiences after each task; (2) Learning by Raw Trajectories: Agents retrieve raw, un-distilled past trajectories; (3) Learning via External Scribing: Agents rely on an external teacher model to distill insights; (4) EvolveR (Ours): A complete, self-contained lifecycle where the agent autonomously distills its own experiences into principles and evolves its policy. Large Language Models (LLMs) have driven the development of autonomous agents capable of solving diverse tasks through advanced reasoning and tool use [1, 2, 3]. However, a significant limitation emerges when these agents engage in sequential tasks: each interaction is treated independently. They approach tasks as isolated episodes, suffering from operational amnesia and failing to learn from past successes or avoid prior mistakes [4]. This inability to leverage experience fundamentally hinders their development toward greater autonomy and intelligence. Humans, by contrast, learn through a continuous lifecycle, leveraging both successes and failures to refine strategies over time [5]. For example, a student solving math problems reflects on recurring errors and successful approaches to extract general problem-solving strategies. This cycle of interaction, reflection, and abstraction is the cornerstone of developing expertise [6]. Endowing LLM agents with a comparable lifecycle is the key to bridging the gap between episodic problem-solving and sustainable self-improvement. While existing frameworks like Retrieval-Augmented Generation (RAG) effectively address knowledge gaps, they fail to solve a more fundamental limitation: the agent’s inability to systematically learn from the consequences of its own interactions [7]. As Figure 1 shows, prior works have attempted to address this limitation, but with critical shortcomings. Researchers store natural language reflections across tasks with a powerful external LLM in an external memory [8, 9]. While resource-efficient, this approach treats such reflections as a transient hint, leaving the agent’s intrinsic policy unchanged. On the other hand, learning by recalling raw cases retrieves entire past trajectories to directly guide decision-making. However, this reliance on raw cases struggles to generalize and, more importantly, fails to abstract. The agent merely mimics past solutions instead of distilling the reusable strategic principles that made them successful [10]. To overcome these challenges, we introduce EvolveR, a framework that enables agents to self-evolve by utilizing their own experiences. EvolveR implements a full experience lifecycle, in which agents collect trajectories through Online Interaction, distill them into a library of abstract strategic principles during Offline Self-Distillation, and subsequently learn to apply these principles to new tasks. Crucially, EvolveR completes the experience lifecycle with a reinforcement learning mechanism that enables the agent to utilize experience. The agent does not merely mimic its past interactions; it evolves based on what it has learned. EvolveR maintains a dynamic experience base where newly distilled principles are semantically deduplicated and continuously evaluated via a metric score that tracks historical effectiveness. We demonstrate EvolveR’s effectiveness on complex question-answering benchmarks, where it significantly outperforms strong agentic baselines. Our contributions can be summarized as follows: - We propose the Experience-Driven Self-Evolution Paradigm, a novel, closed-loop lifecycle for LLM agents. In contrast to agents that forget past interactions, EvolveR systematically integrates a complete cycle of online interaction, offline experiences self-distillation and policy evolution. This process enables the agent to continuously transform raw trajectories into a curated repository of strategic principles, establishing a foundation for adaptive agents. - We introduce a complete system for dynamic experiences curation. This system goes far beyond simple experience storage. It features: (1) a self-distillation mechanism, where the agent autonomously distills principles from previous interactions; and (2) a full maintenance pipeline, including semantic deduplication, integration, and quality control guided by a dynamic metric score. - We provide extensive empirical validation of the EvolveR paradigm across multiple model scales. Our experiments on a diverse suite of complex QA benchmarks demonstrate the effectiveness of our approach. Detailed ablation studies confirm that the synergy of our proposed curation and self-distillation mechanisms is critical to the framework’s success, revealing a key insight: while the self-distillation mechanism is less effective on smaller-scale models, it surpasses distillation by a stronger, external teacher model at the 3B scale, validating the importance of cognitive alignment. 2 Related Work 2.1 Continual Learning and Self-Evolving Agents Continual learning (CL) aims to enable models to learn sequentially while mitigating catastrophic forgetting [11, 12]. While various replay-based and regularization methods have been proposed, most CL paradigms assume predefined task boundaries and focus on knowledge preservation rather than active acquisition in open-ended environments [13, 14, 15, 16]. The pursuit of self-evolving agents moves beyond these limitations by enabling systems to grow autonomously from experience. Frameworks such as Reflexion and Generative Agents explore self-improvement through self-play and reflective reasoning, often storing past trajectories as memory to guide future actions [17, 18, 19, 20, 4]. However, these systems either store raw, unstructured data or rely on memory mechanisms that are not designed for the systematic, long-term distillation and refinement of abstract strategic knowledge. Instead of relying on external data streams, our agent autonomously generates and refines its own experiences through an iterative cycle of online interaction and offline reflection. 2.2 LLM Agents and Reinforcement Learning LLM agents have been widely explored through frameworks such as ReAct, which interleaves reasoning and actions, and Reflecion, which improves task performance via self-reflection [4, 17]. While these approaches are primarily prompt-based and stateless, they prevent long-term accumulation of strategic knowledge. External memory frameworks like ExpeL address this limitation by reusing past trajectories, but they do not enable systematic self-improvement across tasks [8]. While effective, these methods often rely on simple prompting and are inherently stateless, limiting their ability to internalize knowledge across tasks. Recent work has increasingly turned to reinforcement learning (RL) to train agents for long-horizon, multi-turn tasks. However, applying RL is challenging due to sparse rewards and the need for stable training signals. Search-R1 [21], O2-Searcher [22], and AutoRefine [23] all use RL to train LLMs to generate and interact with external search tools. While these works successfully optimize the LLM’s interaction with external factual knowledge, they do not address the broader challenge of an agent’s self-improvement through its own internal experience. 3 Method <details> <summary>x2.png Details</summary>  ### Visual Description ## System Diagram: EvolverR Workflow ### Overview The image presents a system diagram illustrating the workflow of "EvolverR," a process divided into "Online Phase" and "Offline Phase." The diagram details the steps involved in both phases, including data processing, principle generation, and knowledge base updates. The right side of the diagram focuses on the "Search ExpBase" process, detailing how principles are scored, merged, and used to update the knowledge base. ### Components/Axes **Left Side (EvolverR Workflow):** * **Online Phase:** Located at the top-left, indicated by a flame icon and labeled "Parameter Update." * **Offline Phase:** Located at the bottom-left, indicated by a snowflake icon and labeled "Parameter Frozen." * **EvolverR:** Central element of the diagram, representing the core process. * **Workflow Steps:** * Observe (Query) * Think (Analysis... Search Query) * Search ExpBase (Principle) * Search KB (Doc) * Work * Generate Traj * Self-Distill * Get Principle * Update ExpBase * Organize * Consolidate * Perceive **Right Side (Search ExpBase):** * **Search ExpBase:** Title at the top-right, indicated by a magnifying glass icon. * **Principles:** Principle 1, Principle 2, Principle N. * Principle 1: "- For comparison questions, gather data on both items before concluding. - Triple: (Comparison, needs, data on both sides)" * **Scores:** Score:0.7 (for Principle 1), Score:0.6 (for Principle 2), Score:0.9 (for Principle N). * **Trajectories:** Traj1, Traj2 associated with each principle. * **Merge:** Indicates the merging of principles. * **Trajectory:** A box labeled "Trajectory" with a downward arrow to "Summarize". * **New Principle:** A box labeled "New Principle" with a downward arrow to "Retrieve". * **Top k Principles:** A box labeled "Top k Principles" with an arrow leading to a dashed box labeled "Similarity Threshold" and "LLM Semantic Match" containing a robot icon. * **Update Score & Add Traj:** A green box labeled "Update Score & Add Traj" with an upward arrow from the "Similarity Threshold" box labeled "Match". * **Add New Principle:** A green box labeled "Add New Principle" with an upward arrow from the "Similarity Threshold" box labeled "No Match". * **Update ExpBase:** A blue gear icon with the label "Update ExpBase". * **Principle Operation:** A dashed box labeled "Principle Operation" containing the following operations: Distill, Deduplicate, Update, Filter Low-Score. ### Detailed Analysis or ### Content Details **Left Side (EvolverR Workflow):** * The workflow starts with "Observe" and "Query," indicating data input. * "Think" involves analysis and search queries. * "Search ExpBase" and "Search KB" represent knowledge retrieval. * "Work," "Generate Traj," "Self-Distill," "Get Principle," "Organize," "Consolidate," and "Perceive" represent the core processing steps. * "Update ExpBase" closes the loop, updating the knowledge base. **Right Side (Search ExpBase):** * Principles are scored (0.7, 0.6, 0.9) and associated with trajectories (Traj1, Traj2). * Principles are merged, and new principles are added based on similarity thresholds. * The "LLM Semantic Match" component suggests the use of a Large Language Model for semantic matching. * Principle operations include distillation, deduplication, updating, and filtering low-score principles. ### Key Observations * The diagram illustrates a cyclical process with distinct online and offline phases. * The "Search ExpBase" process involves scoring, merging, and updating principles. * The use of "LLM Semantic Match" indicates the application of advanced language models. * The diagram highlights the importance of knowledge base updates and principle refinement. ### Interpretation The diagram depicts a sophisticated system for knowledge evolution and refinement. "EvolverR" continuously updates its knowledge base through online and offline phases, incorporating new information and refining existing principles. The "Search ExpBase" process is crucial for identifying and integrating relevant principles, leveraging semantic matching to ensure accuracy and relevance. The system's ability to distill, deduplicate, and filter principles suggests a focus on maintaining a high-quality knowledge base. The cyclical nature of the workflow indicates a continuous learning and improvement process. </details> Figure 2: Overview of the EvolveR framework’s experience lifecycle. Left: The main loop alternates between an Online Phase, where the agent interacts with the environment and its policy parameters are updated via RL, and an Offline Phase, where the agent’s parameters are frozen and it performs self-distillation and maintains its Experience Base ( $\mathcal{E}$ ). Top Right: A detailed view of the Search ExpBase action, where the agent retrieves scored principles along with their associated trajectories. Bottom Right: The Update ExpBase process, which involves summarizing trajectories and applying a suite of curation operations (distill, deduplicate, update, and filter). In this section, we present EvolveR, a novel framework designed to enable agent self-evolution through a complete, closed-loop experience lifecycle. Inspired by the human cycle of work and reflection, our approach is structured around three core, interconnected components, as depicted in Figure 2. First, in the Offline Experience Self-Distillation phase, the agent’s policy parameters are frozen, and it systematically distills raw trajectories into a curated base of strategic principles. Second, during the Online Interaction phase, the agent applies this distilled wisdom to guide its deliberative reasoning and action, generating new, high-quality interaction data. Finally, the entire cycle is driven by a Policy Evolution mechanism, where the trajectories collected online are used to update the agent’s policy parameters via reinforcement learning, thus closing the loop. This iterative process allows the agent to continuously transform its interactions into evolving expertise. 3.1 Preliminaries: Formalizing Agent Interaction At each state $t$ , the agent, situated in an unknown state $s_{t}$ , selects an action $a_{t}∈\mathcal{A}$ based on its policy. Our agent’s action space $\mathcal{A}$ is designed for complex, knowledge-intensive tasks and comprises three key operations: - <search_experience>: Agent queries its internal experience base $\mathcal{E}$ to retrieve relevant principles distilled from past trajectories. Environment returns retrieved principles as an observation. - <search_knowledge>: Agent queries an external knowledge base (e.g., a search engine) to acquire factual information. Environment returns retrieved information as an observation. - <answer>: Agent outputs its final answer to the problem and concludes the interaction. 3.2 The EvolveR Lifecycle: From Interactions to Principles 3.2.1 Offline Experience Self-Distillation The core of EvolveR is a self-perpetuating lifecycle designed to transform raw interaction data into a strategic principle. This process is divided into two distinct, alternating phases: an offline self-distillation phase for distilling the principle, and an online interaction phase for applying the principle and gathering new interaction data. Principle from Self-Distillation. The process begins with self-distillation. We leverage the agent’s own policy model $\pi_{\theta}$ to analyze its past interaction trajectories. By adopting the persona of an expert through carefully designed prompts, the model reviews each trajectory and, based on its outcome, distills the core strategic insight into a concise natural language statement. This results in either a guiding principle from a success or a cautionary principle from a failure. Inspired by structured memory frameworks such as Mem0 [24] and G-Memory [25], each principle consists of two components: a natural language description paired with several structured knowledge triples, as illustrated in Figure 2. This self-distillation approach enables the agent to autonomously generate reusable knowledge. Deduplication and Integration. To maintain a high-quality experience base ( $\mathcal{E}$ ), we do not add every distilled principle. Instead, each new principle undergoes a rigorous integration process. First, to handle redundancies arising from similar trajectories (e.g., from GRPO sampling), we perform a deduplication step. We use the agent model $\pi_{\theta}$ to pair-wise check for semantic equivalence among newly generated principles that originate from the same problem, keeping only one representative from each semantically equivalent cluster. Second, for each unique principle, we apply a two-stage matching procedure: we first retrieve the most similar existing principles from $\mathcal{E}$ via embedding similarity, then prompt the agent model to provide a binary semantic equivalence judgment. If a principle is novel, it is added as a new entry in $\mathcal{E}$ ; otherwise, the new trajectory is merged under the existing principle, enriching it without introducing redundancy. Let $p_{\text{cand}}$ be a new candidate principle distilled from trajectory $\tau_{\text{src}}$ . We update the experience base $\mathcal{E}$ as follows: $$ \mathcal{E}\leftarrow\begin{cases}\mathcal{E}\cup\{p_{\text{cand}}\}&\text{if }\max_{p\in\mathcal{E}}\text{sim}(p_{\text{cand}},p)<\theta_{\text{sim}}\\ \text{Merge}(\mathcal{E},p^{*},\tau_{\text{src}})&\text{otherwise}\end{cases} \tag{1} $$ where $\text{sim}(·,·)$ is the cosine similarity between principle, $\theta_{\text{sim}}$ is a similarity threshold, and $p^{*}=\text{argmax}_{p∈\mathcal{E}}\text{sim}(p_{\text{cand}},p)$ . The Merge operation links $\tau_{\text{src}}$ to its best match $p^{*}$ . This two-level check ensures that $\mathcal{E}$ grows with novel insights while strengthening existing ones with new evidence. Quality Control via Dynamic Scoring. As the experience base accumulates principles over time, it becomes essential to evaluate their practical utility and prioritize the most effective strategies. To this end, each principle tracks its usage and success counts, enabling the computation of an empirical score that reflects historical performance. We quantify the empirical utility of each principle using a metric score, which is updated as: $$ s(p)=\frac{c_{\text{succ}}(p)+1}{c_{\text{use}}(p)+2} \tag{2} $$ where $c_{\text{succ}}(p)$ and $c_{\text{use}}(p)$ are the success and usage counts for a given principle $p$ , $s(p)$ is the metric score. This score provides a reliable measure of a principle’s historical effectiveness. To ensure the long-term health of the experience base, we periodically prune principles whose scores fall below a threshold $\theta_{\text{prune}}$ . This systematic process of distillation, integration, and quality control ensures that the agent’s wisdom remains a compact and high-quality repository of its most effective strategies. 3.2.2 Online Interaction The online phase serves as the interactive testbed where the agent applies its distilled principles to solve problems. The agent operates within a deliberative reasoning loop (e.g., Think-Act-Observe), which enables it to engage in multi-turn, autonomous tool use. However, the core novelty of EvolveR’s online phase is not the loop itself, but how the principles retrieved from the experience base ( $\mathcal{E}$ ) fundamentally alter the agent’s behavior within it. Experience as a Strategic Principle. Unlike standard agents that must discover reasoning patterns from scratch through trial and error, an EvolveR agent is guided by a strategic wisdom provided by its own past experiences. At any point in its reasoning loop, the agent can issue a <search_experience> action. The retrieved principles $\mathcal{P}_{k}$ do not merely provide factual information; they offer heuristic guidance that shapes the agent’s subsequent reasoning. For instance, retrieving a principle such as “For comparison questions, gather data on both items before concluding,” can directly influence the agent’s internal monologue (<think>) and steer its subsequent potential <search_knowledge> actions. This makes the agent’s exploration more efficient and less prone to common pitfalls, as it learns to follow the wisdom in its own distilled principles. Generating High-Quality Trajectories for Future Distillation. The ultimate purpose of the online phase, within the EvolveR paradigm, extends beyond solving the immediate task. It is responsible for generating high-quality data for the next cycle of offline reflection. Because the agent’s actions are guided by proven principles, the resulting trajectories, $\tau_{\text{new}}$ , are not random walks but are instead rich recordings of structured, experience-guided problem-solving. These trajectories capture the interplay between distilled principles, internal reasoning, and external tool use (e.g., <search_knowledge>), and serve as valuable input for the offline phase, enabling EvolveR to refine existing principles and discover more effective strategies in a virtuous cycle. 3.3 Policy Evolution: Closing the Loop with Reinforcement Learning To enable the agent to learn from its actions and evolve its policy $\pi_{\theta}$ , we employ a reinforcement learning framework. The learning process is guided by a composite reward function and a policy optimization algorithm that leverages the trajectories collected during the online phase. Reward Function. We design a composite reward function $R(\tau)$ for a given trajectory $\tau$ that balances task success with procedural correctness. It is a weighted sum of an outcome reward and a format reward: $R(\tau)=w_{o}R_{\text{outcome}}(\tau)+w_{f}R_{\text{format}}(\tau)$ . <details> <summary>img/grpo.png Details</summary>  ### Visual Description ## Flow Diagram: Model Training Process ### Overview The image is a flow diagram illustrating a model training process. It depicts the flow of information and computation between different components, including input questions, knowledge bases, policy models, reward models, and reference models, ultimately leading to a set of answers. The diagram also shows the use of KL divergence as a feedback mechanism. ### Components/Axes * **Input:** * `Questions` (light blue rounded rectangle) * **Main Components (beige background):** * `Policy Model` (green rounded rectangle) * `Experience Base` (white rounded rectangle) * `Knowledge Base` (white rounded rectangle) * `O1`, `O2`, ..., `OG` (white rounded rectangles) * `R1`, `R2`, ..., `RG` (white rounded rectangles) * `A1`, `A2`, ..., `AG` (white rounded rectangles) * **Reference and Reward:** * `GT Answers` (light blue rounded rectangle) * `Reference Model` (light brown rounded rectangle) * `Reward Model` (dashed border, light gray rounded rectangle) * `Outcome Reward` (white rounded rectangle) * `Format Reward` (white rounded rectangle) * **Process Labels:** * `Group Computation` * **Feedback:** * `KL` (Kullback-Leibler divergence) ### Detailed Analysis 1. **Input:** The process begins with `Questions` (light blue) which are fed into a system with a `Policy Model` (green), `Experience Base` (white), and `Knowledge Base` (white). 2. **Policy Model Interaction:** The `Policy Model` interacts with both the `Experience Base` and the `Knowledge Base`, indicated by two-way arrows. 3. **Output Generation:** The output from the `Experience Base` is a series of elements `O1`, `O2`, ..., `OG`. 4. **Reward Mechanism:** * The `Reference Model` (light brown) receives input from `O1`, `O2`, ..., `OG`. * The `Reward Model` (light gray, dashed border) consists of `Outcome Reward` and `Format Reward`. It receives input from `O1`, `O2`, ..., `OG`. * The output of the `Reward Model` is a series of elements `R1`, `R2`, ..., `RG`. 5. **Group Computation:** The elements `R1`, `R2`, ..., `RG` undergo `Group Computation` to produce the final answers `A1`, `A2`, ..., `AG`. 6. **Feedback Loop:** * The `GT Answers` (light blue) and the `Reference Model` (light brown) provide feedback to the `Policy Model` (green) via `KL` divergence. * The `GT Answers` (light blue) also provide feedback to the `Reward Model` (light gray, dashed border) via `KL` divergence. * The `GT Answers` (light blue) also provide feedback to the `Group Computation` via `KL` divergence. ### Key Observations * The diagram illustrates a closed-loop system where the `Policy Model` is continuously refined based on feedback from the `Reference Model`, `Reward Model`, and `GT Answers`. * The `Reward Model` is composed of two distinct reward types: `Outcome Reward` and `Format Reward`, suggesting a multi-faceted evaluation of the generated outputs. * The use of `KL` divergence indicates a method for comparing probability distributions, likely between the generated outputs and the ground truth answers. ### Interpretation The diagram represents a reinforcement learning or imitation learning framework for training a model to answer questions. The `Policy Model` generates answers based on its current state, informed by the `Experience Base` and `Knowledge Base`. The `Reference Model` and `Reward Model` evaluate these answers, providing feedback to refine the `Policy Model`. The `GT Answers` serve as the gold standard for evaluation. The `KL` divergence is used to quantify the difference between the model's output distribution and the ground truth distribution, guiding the learning process. The `Group Computation` step suggests that the final answers are generated by aggregating or processing the individual rewards. This setup allows the model to learn both the content and the format of the answers, improving its overall performance. </details> Figure 3: Policy model update optimization algorithm of EvolveR. - Outcome Reward $R_{\text{outcome}}$ , is a sparse, binary reward based on the final answer’s correctness. Following prior work, it is determined by an exact match with the ground truth: $$ R_{\text{outcome}}(\tau)=\text{EM}(a_{\text{pred}},a_{\text{gold}}) \tag{3} $$ where $a_{\text{pred}}$ is the final answer extracted from the trajectory $\tau$ and $a_{\text{gold}}$ is the ground truth answer. - Format Reward $R_{\text{format}}$ , is a dense shaping reward that evaluates the quality of the reasoning process. Let $N_{\text{think}}(\tau)$ , $N_{\text{exp}}(\tau)$ and $N_{\text{know}}(\tau)$ denote the counts of valid <think>, <search_experience> and <search_knowledge> actions within $\tau$ . $R_{\text{format}}$ is composed of a think score $R_{\text{think}}$ , rewarding a balanced number of reasoning steps, and a search score $R_{\text{search}}$ promoting search experience and knowledge. The final format reward is calculated as: $$ R_{\text{format}}(\tau)=\mathbb{I}(\tau_{\text{complete}})\cdot\frac{R_{\text{think}}(\tau)+R_{\text{search}}(\tau)}{2} \tag{4} $$ where $\mathbb{I}(\tau_{\text{complete}})$ is an indicator function that is $1$ only if the trajectory contains at least one of each required action type (<think>, any search, and <answer>), and $0$ otherwise. This ensures that only structurally complete trajectories receive a format reward. Policy Optimization. The policy $\pi_{\theta}$ is updated using the collected trajectories. We utilize Group Relative Policy Optimization (GRPO) [26], which balances the optimization stability and efficiency by using the average reward of multiple sampled trajectories as a baseline, thus avoiding the need for a learned value function. Specifically, for each input, we sample a group of $G$ trajectories. The policy is then optimized by maximizing the following objective function: $$ \mathcal{J}_{\text{GRPO}}(\theta)=\mathbb{E}_{\tau\in\mathcal{D}}\left[\sum_{t=1}^{|\tau|}\min\left(\rho_{t}(\theta)\hat{A}_{t},\text{clip}(\rho_{t}(\theta),1-\epsilon,1+\epsilon)\hat{A}_{t}\right)-\beta D_{\text{KL}}[\pi_{\theta}||\pi_{\text{ref}}]\right] \tag{5} $$ where $\rho_{t}(\theta)=\frac{\pi_{\theta}(a_{t}|h_{t})}{\pi_{\text{old}}(a_{t}|h_{t})}$ is the importance sampling ratio, $\hat{A}_{t}$ is the advantage estimate, and the final term is a KL-divergence penalty to constrain policy updates. Crucially, this optimization process is deeply integrated with our experience lifecycle. As the agent’s actions during the online phase are conditioned on the principles $\mathcal{P}_{k}$ retrieved from its experience base, the trajectories collected in $\mathcal{D}$ are inherently experience-guided. Consequently, the GRPO update does not merely learn a generic reasoning policy. Instead, it explicitly learns a policy of how to effectively utilize its own distilled wisdom to generate successful outcomes. The optimization process, therefore, reinforces the valuable connections between retrieving high-quality principles and producing high-reward trajectories, successfully closing the learning loop. 4 Experiments 4.1 Experimental Implementation Details 4.1.1 Tasks and Datasets To comprehensively evaluate the EvolveR paradigm, we assess its performance on seven question-answering benchmarks, encompassing both in-domain and out-of-domain datasets. Following prior work [21, 22], the in-domain datasets, whose training splits are used to build the experience base, include Natural Questions (NQ) [27] and the multi-hop benchmark HotpotQA [28]. The out-of-domain datasets, used exclusively for evaluating generalization, encompass the general QA benchmarks TriviaQA [29] and PopQA [30], as well as the more complex multi-hop challenges 2WikiMultiHopQA [31], Musique [32], and Bamboogle [33]. 4.1.2 Baseline Methods Following prior works, we compare against a comprehensive suite of baselines built upon the Qwen2.5 foundational models. The baselines represent three primary paradigms. First, prompting-based methods, which require no parameter updates, include Direct Inference, Chain-of-Thought (CoT) [18], Retrieval-Augmented Generation (RAG) [34], and advanced variants like IRCoT [35] and Search-o1 [36]. Second, Supervised Fine-Tuning (SFT) methods represent approaches that learn from static expert data, including standard SFT [37] and Rejection Sampling [38]. Finally, the most direct competitors are RL methods, against which we benchmark extensively. This category is primarily composed of Search-R1 [21], DeepSeek-R1 [39], which are also trained with trajectory-level feedback. Specifically, DeepSeek-R1 performs reasoning and answer steps without a search engine, whereas Search-R1 incorporates an external local or web search engine. Together, these baselines provide a challenging evaluation landscape for our proposed paradigm. 4.1.3 Evaluation Metrics To ensure a direct and fair comparison with prior work in our main results, our primary evaluation metric is Exact Match (EM), a strict measure that requires the predicted answer to exactly match the ground truth after standard normalization. We also report the F $1$ Score in the analysis of model scales’ generalizability, which provides a more comprehensive and robust measure of performance, particularly since ground truths may contain multiple valid answers or aliases. 4.1.4 Implementation Details Our experiments are conducted on the Qwen2.5 model family. Inspired by DeepSeek-R1 [39], we introduce a cold-start stage to stabilize early RL training by first fine-tuning the base model on a small, curated dataset of CoT interaction trajectories. Following the setup of Search-R1, we construct this dataset from approximately 700 samples from the NQ and HotpotQA training sets. We utilize the LLama_Factory [40] to fine-tune the model with LoRA. For the agent evolution phase, we employ GRPO for optimization. At each RL step, we sample a batch of 128 prompts, generating $G=8$ trajectories for each. The agent is then updated, again using Adam, but with a reduced learning rate of $1× 10^{-6}$ , a warm-up step of 20 and a mini-batch size of 128. All training is conducted on 8 A100 GPUs, leveraging the Verl framework https://github.com/volcengine/verl for efficient implementation. We will show more details in Appendix 4.1. 4.2 Main Results The main results of our evaluation are presented in Table 1. Our analysis focuses on the comprehensive evaluation conducted on the Qwen2.5-3B model family (we will show more results of different model scales in the 5.1). EvolveR achieves the highest average score (0.382) in the 3B scale, outperforming all baselines, including strong RL agents like Searcher-R1. This robust overall performance is not driven by a narrow specialty, but by consistent, top-tier results across a wide spectrum of tasks; it secures the best scores on diverse benchmarks, including the in-domain NQ, the out-of-domain PopQA, and the adversarial Bamboogle dataset, while remaining highly competitive on all others. This consistent, high-level performance across diverse benchmarks validates that by systematically distilling, managing, and utilizing, agents can develop more generalizable and powerful problem-solving strategies. Table 1: Main results on QA benchmarks. The best performance in each column is set in bold. Our proposed model, EvolveR, is highlighted in gray. | Direct Inference CoT IRCoT | 0.106 0.023 0.111 | 0.149 0.021 0.164 | 0.288 0.032 0.312 | 0.108 0.005 0.200 | 0.244 0.021 0.171 | 0.020 0.002 0.067 | 0.024 0.000 0.240 | 0.134 0.015 0.181 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Search-o1 | 0.238 | 0.221 | 0.472 | 0.262 | 0.218 | 0.054 | 0.320 | 0.255 | | RAG | 0.348 | 0.255 | 0.544 | 0.387 | 0.226 | 0.047 | 0.080 | 0.270 | | SFT | 0.249 | 0.186 | 0.292 | 0.104 | 0.248 | 0.044 | 0.112 | 0.176 | | R1-base | 0.226 | 0.201 | 0.455 | 0.173 | 0.268 | 0.055 | 0.224 | 0.229 | | R1-instruct | 0.210 | 0.208 | 0.449 | 0.171 | 0.275 | 0.060 | 0.192 | 0.224 | | Rejection Sampling | 0.294 | 0.240 | 0.488 | 0.332 | 0.233 | 0.059 | 0.210 | 0.265 | | Search-R1-base | 0.406 | 0.284 | 0.587 | 0.435 | 0.273 | 0.049 | 0.088 | 0.303 | | Search-R1-instruct | 0.341 | 0.324 | 0.545 | 0.378 | 0.319 | 0.103 | 0.264 | 0.325 | | EvolveR (ours) | 0.434 | 0.373 | 0.584 | 0.434 | 0.381 | 0.137 | 0.328 | 0.382 | 5 Further Analysis 5.1 Analysis of Model Scales Generalizability <details> <summary>img/evolver_scaling_results.png Details</summary>  ### Visual Description ## Chart: EM and F1 Scores on Various Datasets ### Overview The image is a bar and line chart comparing the EM (Exact Match) and F1 scores of a model on various datasets. The x-axis represents the datasets, and the y-axes represent the EM score (left) and F1 score (right). The chart displays the performance of the model with different parameter sizes (0.5B, 1.5B, and 3B). ### Components/Axes * **X-axis:** Datasets (NQ, HotpotQA, TriviaQA, PopQA, 2wiki, Musique, Bamboogle) * **Left Y-axis:** EM Score, ranging from 0.0 to 0.8 in increments of 0.1. * **Right Y-axis:** F1 Score, ranging from 0.0 to 0.8 in increments of 0.1. * **Legend (top-right):** * EM (0.5B) - Light Purple Bar * EM (1.5B) - Light Blue Bar * EM (3B) - Dark Blue Bar * F1 (0.5B) - Light Yellow Dashed Line with Circle Markers * F1 (1.5B) - Light Orange Dashed Line with Circle Markers * F1 (3B) - Dark Orange Dashed Line with Circle Markers ### Detailed Analysis **EM Scores (Bar Charts):** * **NQ:** * EM (0.5B): ~0.2 * EM (1.5B): ~0.36 * EM (3B): ~0.44 * **HotpotQA:** * EM (0.5B): ~0.11 * EM (1.5B): ~0.26 * EM (3B): ~0.38 * **TriviaQA:** * EM (0.5B): ~0.23 * EM (1.5B): ~0.52 * EM (3B): ~0.59 * **PopQA:** * EM (0.5B): ~0.26 * EM (1.5B): ~0.4 * EM (3B): ~0.44 * **2wiki:** * EM (0.5B): ~0.16 * EM (1.5B): ~0.21 * EM (3B): ~0.39 * **Musique:** * EM (0.5B): ~0.03 * EM (1.5B): ~0.07 * EM (3B): ~0.14 * **Bamboogle:** * EM (0.5B): ~0.06 * EM (1.5B): ~0.13 * EM (3B): ~0.34 **F1 Scores (Line Charts):** * **F1 (0.5B) - Light Yellow Dashed Line:** * NQ: ~0.25 * HotpotQA: ~0.17 * TriviaQA: ~0.3 * PopQA: ~0.3 * 2wiki: ~0.21 * Musique: ~0.06 * Bamboogle: ~0.23 * **F1 (1.5B) - Light Orange Dashed Line:** * NQ: ~0.52 * HotpotQA: ~0.48 * TriviaQA: ~0.67 * PopQA: ~0.44 * 2wiki: ~0.45 * Musique: ~0.21 * Bamboogle: ~0.44 * **F1 (3B) - Dark Orange Dashed Line:** * NQ: ~0.44 * HotpotQA: ~0.37 * TriviaQA: ~0.58 * PopQA: ~0.39 * 2wiki: ~0.26 * Musique: ~0.12 * Bamboogle: ~0.13 ### Key Observations * For both EM and F1 scores, performance generally increases with the parameter size (0.5B < 1.5B < 3B). * TriviaQA consistently shows high EM and F1 scores across all parameter sizes. * Musique and Bamboogle datasets have relatively low EM scores compared to other datasets. * The F1 score for 1.5B parameters is generally higher than the F1 score for 3B parameters. ### Interpretation The chart illustrates the impact of model size on the performance of question-answering models across different datasets. The increase in EM and F1 scores with larger parameter sizes (0.5B to 3B) suggests that larger models are better at understanding and answering questions. However, the F1 score for 1.5B parameters being higher than the F1 score for 3B parameters suggests that there may be a point of diminishing returns or overfitting with larger models, especially for the F1 score. The varying performance across datasets indicates that some datasets are inherently more challenging for the model. TriviaQA appears to be a relatively easier dataset, while Musique and Bamboogle are more difficult. </details> Figure 4: Performance of EvolveR across various model scales. To validate that our EvolveR framework is a generalizable paradigm rather than a method tailored to a specific model size, we evaluated its performance across a spectrum of open-source model scales. As presented in Figure 4, we applied EvolveR to Qwen2.5 models of 0.5B, 1.5B, and 3B parameters. The results reveal a clear and consistent positive trend: as the parameter count of the base model increases, the performance of the EvolveR agent improves significantly on every benchmark. The average performance rises monotonically from 0.150 on the 0.5B model to 0.270 on the 1.5B model, and further to 0.382 on the 3B model. This scaling behavior demonstrates that our experience-driven lifecycle effectively harnesses the superior reasoning and instruction-following capabilities inherent in larger foundational models. It confirms that EvolveR acts as a synergistic layer on top of the base model, and suggests that its performance will continue to improve with future advancements in the open-source LLM landscape. 5.2 Ablation Studies: Dissecting the EvolveR Framework 5.2.1 Validating the Self-Distillation Mechanism A central claim of our work is that an agent can learn effectively through self-distillation. To rigorously investigate this, we compare our standard EvolveR (self-distill) against a strong alternative, EvolveR(teacher-distill), which uses the powerful GPT-4o-mini as an external model for experience distillation. The results, presented in Table 2, reveal a nuanced, scale-dependent relationship. For smaller models like the 0.5B variant, the stronger external teacher provides a clear benefit, as the base model’s own distillation capabilities are limited. However, as the model scales to 3B, a reversal occurs: our EvolveR (self-distill) (0.382 avg.) outperforms the teacher-guided variant (0.370 avg.). This is a critical finding, suggesting that as an agent’s own reasoning becomes more sophisticated, principles distilled from its own internal policy are ultimately more effective due to better ”cognitive alignment”. This validates self-distillation as a core, scaling strength of the EvolveR paradigm. Table 2: Validating the self-distillation mechanism. We compare our EvolveR, which uses its own model for distillation, against a variant that uses a larger, external model (GPT-4o-mini). | Model Variant | In domain | Out of domain | Avg. | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | NQ $\uparrow$ | HotpotQA $\uparrow$ | TriviaQA $\uparrow$ | PopQA $\uparrow$ | 2wiki $\uparrow$ | Musique $\uparrow$ | Bamboogle $\uparrow$ | | | | Qwen2.5-0.5B | | | | | | | | | | EvolveR (self-distill) | 0.194 $\downarrow$ | 0.114 $\uparrow$ | 0.233 $\uparrow$ | 0.262 $\uparrow$ | 0.160 $\downarrow$ | 0.029 $\downarrow$ | 0.056 $\downarrow$ | 0.150 $\downarrow$ | | EvolveR (teacher-distill) | 0.281 $\uparrow$ | 0.193 $\uparrow$ | 0.402 $\uparrow$ | 0.363 $\uparrow$ | 0.202 $\uparrow$ | 0.033 $\uparrow$ | 0.064 $\uparrow$ | 0.220 $\uparrow$ | | Qwen2.5-1.5B | | | | | | | | | | EvolveR (self-distill) | 0.358 $\uparrow$ | 0.257 $\uparrow$ | 0.510 $\uparrow$ | 0.389 $\uparrow$ | 0.188 $\uparrow$ | 0.057 $\uparrow$ | 0.128 $\uparrow$ | 0.270 $\uparrow$ | | EvolveR (teacher-distill) | 0.352 $\downarrow$ | 0.259 $\uparrow$ | 0.503 $\downarrow$ | 0.395 $\uparrow$ | 0.207 $\uparrow$ | 0.072 $\uparrow$ | 0.240 $\uparrow$ | 0.290 $\uparrow$ | | Qwen2.5-3B | | | | | | | | | | EvolveR (self-distill) | 0.434 $\uparrow$ | 0.373 $\uparrow$ | 0.584 $\uparrow$ | 0.434 $\uparrow$ | 0.381 $\uparrow$ | 0.137 $\uparrow$ | 0.328 $\uparrow$ | 0.382 $\uparrow$ | | EvolveR (teacher-distill) | 0.421 $\downarrow$ | 0.372 $\downarrow$ | 0.583 $\downarrow$ | 0.359 $\downarrow$ | 0.437 $\uparrow$ | 0.127 $\downarrow$ | 0.288 $\downarrow$ | 0.370 $\downarrow$ | 5.2.2 The Role of Experience Retrieval To quantify the direct benefit of providing the agent with access to its distilled principles at inference time. To achieve this, we compare our full EvolveR model against an ablated variant, EvolveR w/o exp-retrieve. It is critical to note that both models undergo the identical experience-driven RL training process. The sole difference is that the w/o exp-retrieve variant is denied access to the experience base during evaluation. The results in Table 3 show a stark performance degradation across all model scales when experience retrieval is disabled. For the 3B model, for instance, the average performance drops significantly from 0.382 to 0.340. This substantial gap underscores a key finding: an agent trained with our EvolveR framework, while powerful on its own, achieves its full potential only when it can access and condition on the relevant principles from its past. This demonstrates that experience retrieval is a critical and indispensable component of the EvolveR paradigm for optimal performance. Table 3: Investigating the role of experience retrieval at inference time. The w/o exp-retrieve variant uses the same model but is not allowed to access the experience base during evaluation. | Model Variant | In domain | Out of domain | Avg. | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | NQ $\uparrow$ | HotpotQA $\uparrow$ | TriviaQA $\uparrow$ | PopQA $\uparrow$ | 2wiki $\uparrow$ | Musique $\uparrow$ | Bamboogle $\uparrow$ | | | | Qwen2.5-0.5B | | | | | | | | | | EvolveR | 0.194 $\downarrow$ | 0.114 $\downarrow$ | 0.233 $\downarrow$ | 0.262 $\downarrow$ | 0.160 $\downarrow$ | 0.029 $\downarrow$ | 0.056 $\downarrow$ | 0.150 $\downarrow$ | | EvolveR w/o exp-retrieve | 0.085 $\downarrow$ | 0.065 $\downarrow$ | 0.137 $\downarrow$ | 0.150 $\downarrow$ | 0.082 $\downarrow$ | 0.013 $\downarrow$ | 0.016 $\downarrow$ | 0.078 $\downarrow$ | | Qwen2.5-1.5B | | | | | | | | | | EvolveR | 0.358 $\downarrow$ | 0.257 $\downarrow$ | 0.510 $\downarrow$ | 0.389 $\downarrow$ | 0.188 $\downarrow$ | 0.057 $\downarrow$ | 0.128 $\downarrow$ | 0.270 $\downarrow$ | | EvolveR w/o exp-retrieve | 0.136 $\downarrow$ | 0.112 $\downarrow$ | 0.218 $\downarrow$ | 0.160 $\downarrow$ | 0.136 $\downarrow$ | 0.019 $\downarrow$ | 0.080 $\downarrow$ | 0.123 $\downarrow$ | | Qwen2.5-3B | | | | | | | | | | EvolveR | 0.434 $\downarrow$ | 0.373 $\downarrow$ | 0.584 $\downarrow$ | 0.434 $\downarrow$ | 0.381 | 0.137 $\downarrow$ | 0.328 $\downarrow$ | 0.382 $\downarrow$ | | EvolveR w/o exp-retrieve | 0.405 $\downarrow$ | 0.343 $\downarrow$ | 0.569 $\downarrow$ | 0.392 $\downarrow$ | 0.334 $\downarrow$ | 0.100 $\downarrow$ | 0.240 $\downarrow$ | 0.340 $\downarrow$ | 6 Conclusion In this work, we introduced EvolveR, a novel paradigm for self-evolving LLM agents centered on a complete, closed-loop experience lifecycle. 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[2024] Jianlv Chen, Shitao Xiao, Peitian Zhang, Kun Luo, Defu Lian, and Zheng Liu. Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. arXiv preprint arXiv:2402.03216, 2024. Appendix A Appendix A.1 Experimental Implementation Details We provide a comprehensive list of hyperparameters and implementation details used in our experiments to ensure full reproducibility. General Setup. Across all experiments, we use models from the Qwen2.5 family [41] with their corresponding tokenizers. The maximum sequence length is set to 8192 tokens for all inputs, and the maximum response sequence length is set to 1024 tokens. The GPT-4o-mini model is used as the teacher model in the corresponding ablation study. We use BGE-M3 [42] as our embedding model. Cold-start Stage. This SFT stage is conducted for 3 epochs using the Adam optimizer, with an initial learning rate of $1× 10^{-4}$ , a warm-up ratio of 0.1, and a batch size of 16. Online Interaction Phase. For each <search_knowledge> action, we retrieve the top- $k_{d}=3$ documents from the external knowledge base, following the prior work [21]. Similarly, for each <search_experience> action, we retrieve the top- $k_{e}=3$ principles from the experience base $\mathcal{E}$ . Offline Distill Phase. The self-distill mechanism utilizes the agent’s own policy model $\pi_{\theta}$ to distill principles. The temperature is set to 1 during this phase. For the deduplication and integration process, we first use a semantic similarity pre-filter with a threshold of $\theta_{\text{sim}}=0.85$ before passing candidates to the LLM-based equivalence check. The periodic principle sweep removes any principle from $\mathcal{E}$ whose metric_score falls below the pruning threshold of $\theta_{\text{prune}}=0.3$ . Reward Function Details. As described in the Section 3.3, the Format Reward is an average of a think score and a search score. We detail their specific calculation here. The think score $R_{\text{think}}$ is determined by a discrete mapping based on the number of <think> actions, $N_{\text{think}}$ : it scales from 0.2 (for $N_{\text{think}}=1$ ) to a maximum of 1.0 (for $N_{\text{think}}=6$ ), and is capped at 0.5 for excessive reasoning ( $N_{\text{think}}>8$ ) to encourage conciseness. The search score $R_{\text{search}}$ is the sum of a diversity score and a quantity bonus. The diversity score is 0.5 if both <search_experience> and <search_knowledge> are used, 0.2 if only one type is used, and 0 otherwise. A quantity bonus of 0.1 is added for each additional search action beyond the first, up to a maximum bonus of 0.5 (for a total of 6 searches). Policy Optimization. The composite reward function is weighted with $w_{o}=1.0$ for the outcome reward and $w_{f}=0.1$ for the format reward. For the GRPO objective function (Equation 5), the clipping parameter is set to $\epsilon=0.2$ and the KL-divergence coefficient is $\beta=0.001$ . During the training procedure, we adopt vLLM to accelerate LLM rollouts. The tensor parallel size is set to 1, and the GPU memory utilization ratio is set at 0.6. For rollout sampling, we use a temperature of 1.0 and a top-p value of 0.95. A.2 Prompt Details A.2.1 Cold Start Prompt The Prompt in Table 5 is used during the cold-start stage to generate the initial trajectories for SFT. This prompt guides a powerful LLM (we used GPT-4o) to act as an expert problem-solver, producing a small dataset with right format trajectories to cold start. A.2.2 System Prompt The Prompt in Table 4 is the system prompt used by the EvolveR agent during the online interaction phase. It defines the agent’s core identity, its available actions (<think>, <search_knowledge>, <search_experience>, <answer>), and the overall format for its reasoning process. A.2.3 Distill Principle Prompt The Prompt in Table 6 and Table 7 are used during the offline experience self-distillation phase to enable the agent’s self-distillation mechanism. Based on the outcome of a trajectory, one of two distinct prompts is used to guide the agent’s own model ( $\pi_{\theta}$ ) to distill a principle. The first Prompt is for successful trajectories, focusing on extracting a guiding principle. The second is for failed trajectories, aimed at formulating a cautionary principle. A.2.4 Judge Same Principle Prompt The Prompt in Table 8 is a crucial component of the deduplication and integration process within the offline experience self-distillation. It tasks the agent’s own model ( $\pi_{\theta}$ ) with acting as a semantic judge. Given two principles (a newly distilled candidate and a retrieved existing similar one), the model is asked to determine if they are semantically equivalent. The binary ”yes/no” output of this Prompt is used to decide whether to merge a new experience or create a new principle. Table 4: System prompt for LLM agents | Answer the given question. | | --- | | You must conduct reasoning inside <think> and </think> first every time you get new information or get new experience principles. | | After reasoning, you can search for past experiences by <search_experience> query </search_experience> to get relevant past experience principles (may be guilding or warning principles) and it will return the top searched results between <experience> and </experience>. | | You can use these principles which you think is helpful to help you answer the question. | | If you find you lack some knowledge, you can call a search engine by <search_knowledge> query </search_knowledge> and it will return the top searched results between <information> and </information>. | | You can search knowledge and experience as many times as your want. | | If you find no further external knowledge needed, you can directly provide the answer inside <answer> and </answer>, without detailed illustrations. | | For example, <answer> Beijing </answer> | | User: {question} | Table 5: Prompt for cold start. | You are a top-notch intelligent reasoning expert, adept at restoring solution paths from given answers and documents in reverse. Your task is to simulate a full reasoning trajectory for answering the question below, based on the provided documents and answer. You must reason step-by-step as if you do not yet know the final answer, even though it is given for supervision. | | --- | | In <think> blocks, do not reference or confirm the final answer directly. Instead, reason like a human—understand the task, recall prior knowledge, evaluate the need for experience or external information, and gradually infer the answer. | | The reasoning trajectory must follow the **exact format below**. If the retrieved **experience alone is sufficient to answer the question**, you may skip the <search_knowledge> and <information> steps. | | Output Format: | | <think> … </think> | | <search_experience> | | - Retrieve 2–3 relevant abstract experience principles, using structured triple format. | | - For each principle, add a short description of its purpose. | | </search_experience> | | <think> Explain what you plan to do after retrieving experience. Decide whether you still need to retrieve knowledge. </think> | | [IF experience is enough:] | | <think> | | - List the principles you are applying, include their triple form and description. | | - Explain briefly how each principle contributes to your reasoning. | | - Continue with reasoning based on these principles and conclude with your final judgment. | | </think> | | <answer> … </answer> | | [ELSE:] | | <search_knowledge> | | - Generate one or more natural language search queries that would help retrieve the provided documents. | | </search_knowledge> | | <information> | | {relevant_document} | | </information> | | <think> Reflect on retrieved information. </think> | | <think> | | - List the principles you are applying, include their triple form and description. | | - Explain how each principle guides the reasoning process using the retrieved information. | | - Summarize your reasoning path and justify the answer. | | </think> | | <Answer> … </Answer> | | Inputs: | | Query: {query} | | Relevant Documents: {relevant_document} | | Answer: {answer} | | Please begin generating the reasoning trajectory. | Table 6: Prompt for summarizing a successful interaction trajectory. | You are an expert in analyzing interaction logs to distill generalizable wisdom. | | --- | | Analyze the following successful interaction trajectory. Your goal is to extract a ”Guiding Principle” from it. | | A ”Guiding Principle” has two parts: | | 1. A concise, one-sentence natural language description. This is the core advice. | | 2. A structured representation of the key steps or logic, as a list of simple (subject, predicate, object) triplets. | | [Trajectory Log]: {{trajectory_log}} | | Final Outcome: SUCCESS | | Your Task: | | Based on the trajectory, generate the Guiding Principle. | | First, on a new line, write {DESCRIPTION_PART_SEPARATOR}. | | Then, write the one-sentence description of the pitfall. | | Then, on a new line, write {STRUCTURED_PART_SEPARATOR}. | | Finally, provide the structured triplets describing the failure pattern in a valid JSON list format. | | [Example]: | | {DESCRIPTION_PART_SEPARATOR} | | When a file download fails with a 404 error, do not immediately retry the download; instead, verify the source URL’s validity first. | | {STRUCTURED_PART_SEPARATOR} [ (file download, results_in, 404 error), (immediate_retry, is, ineffective), (correct_action, is, verify URL) ] [Output]: | Table 7: Prompt for summarizing a failed interaction trajectory. | You are an expert in analyzing interaction logs to find the root cause of failures. | | --- | | Analyze the following failed interaction trajectory. Your goal is to extract a ”Cautionary Principle” from it. | | A ”Cautionary Principle” has two parts: | | 1. A concise, one-sentence description of the key mistake to avoid and under what circumstances. | | 2. A structured representation of the failure pattern, as a list of simple (subject, predicate, object) triplets. | | [Trajectory Log]: {{trajectory_log}} | | Final Outcome: FAILURE | | Your Task: | | Based on the trajectory, generate the Cautionary Principle. | | First, on a new line, write {DESCRIPTION_PART_SEPARATOR}. | | Then, write the one-sentence description of the pitfall. | | Then, on a new line, write {STRUCTURED_PART_SEPARATOR}. | | Finally, provide the structured triplets describing the failure pattern in a valid JSON list format. | | [Example]: | | {DESCRIPTION_PART_SEPARATOR} | | When a file download fails with a 404 error, do not immediately retry the download; instead, verify the source URL’s validity first. | | {STRUCTURED_PART_SEPARATOR} [ (file download, results_in, 404 error), (immediate_retry, is, ineffective), (correct_action, is, verify URL) ] [Output]: | Table 8: Prompt for Principle Similarity Analysis. | You are a semantic analysis expert. Determine if two principles describe the same core idea, even if they use different words. | | --- | | Principle A: “{summary}” | | Principle B: “{existing_principle_description}” | | Do Principle A and Principle B describe the same essential advice or warning? | | Please answer with only “Yes” or “No”. | A.3 Exploring the Influence of Experience Internalization In our proposed framework, all retrieved information (both from the external knowledge base (<information>) and our internal experience base (<experience>)) is treated as context, with loss masked during the model update phase. A natural question arises from this design: while it is sensible to avoid learning the content of transient external documents, could the agent benefit from directly ‘absorbing‘ its own distilled wisdom into its parameters? To explore this, we conducted a supplementary experiment on the Qwen2.5-3B model. We created a variant, EvolveR w/ exp-absorb, where we selectively unmasked the loss for the retrieved <experience> tokens, allowing the learning signal to flow through them. Our hypothesis was that this might enable the agent to internalize the strategic logic of its principles. The results, presented in Table 9, were insightful. The EvolveR w/ exp-absorb variant exhibited a slight performance degradation compared to our standard approach. We posit that this is due to the challenge of noise in the training signal. In our current implementation, the agent retrieves a set of top- $k$ principles at each step. Not all of these principles may be perfectly relevant to the immediate context. By directly internalizing all retrieved principles without a dynamic quality filter, the agent risks being updated with noisy or even counter-productive signals. This finding suggests that for direct internalization to be effective, it may require more sophisticated mechanisms, such as an auxiliary model to weigh the relevance of each principle before absorption. We believe that developing such mechanisms is a promising future direction towards achieving a fully autonomous cycle of self-exploration, self-distillation, and self-absorption. Table 9: Ablation study on the experience internalization mechanism. EvolveR w/o exp-absorb treats principles as external context by masking gradients during backpropagation. | EvolveR EvolveR w exp-absorb | 0.434 $\downarrow$ 0.433 $\downarrow$ | 0.373 $\downarrow$ 0.369 $\downarrow$ | 0.584 $\downarrow$ 0.583 $\downarrow$ | 0.434 $\uparrow$ 0.435 $\uparrow$ | 0.381 $\downarrow$ 0.376 $\downarrow$ | 0.137 $\downarrow$ 0.124 $\downarrow$ | 0.328 $\downarrow$ 0.280 $\downarrow$ | 0.382 $\downarrow$ 0.371 $\downarrow$ | | --- | --- | --- | --- | --- | --- | --- | --- | --- | A.4 Case Study of EvolveR We provide a detail rollout case of EvolveR in Table 10 Table 10: A rollout case of EvolveR | System: System_Prompt | | --- | | Question: who was the pinkerton detective agency’s first female detective? | | Ground Truth: Kate Warne | | EvolveR: <think> I need to determine who was the first female detective employed by the Pinkerton Detective Agency. This requires identifying the historical context and any notable female figures associated with this agency. Based on prior reasoning experience, I anticipate some potentially useful principles:- Principle 1: (Historical Agency, First Female Employee, Female Detective) - This principle helps identify the first female to hold a specific role within a historical organization. - Principle 2: (Organization, Historical Role, Notable Individual) - This principle assists in linking an organization to a notable individual who played a significant role in its history. </think> <search_experience> - Principle 1: (Historical Agency, First Female Employee, Female Detective) - This principle helps identify the first female to hold a specific role within a historical organization. - Principle 2: (Organization, Historical Role, Notable Individual) - This principle assists in linking an organization to a notable individual who played a significant role in its history. </search_experience> | | Env: <experience> [Principle 0], type: guiding, metric score: 0.67, description: To identify the first woman to hold a specific significant position within an organization or entity, connect the historical narrative context and mention which event or timeline may signify the position in question… <experience> | | EvolveR: <think> Based on the experience principles, I plan to apply the principles related to identifying the first female employee in a historical organization and linking this to notable figures. I will also consider using historical documents or specific timelines to confirm the answer. </think> <search_knowledge> - Query: who was the first female detective employed by the Pinkerton Detective Agency? </search_knowledge> | | Env: <information> Doc 1(Title: ”Kate Warne”)… Doc 2(Title: ”Private investigator”) … Doc 3(Title: ”Kate Warne”)… </information> | | EvolveR: <think> Based on the retrieved documents, I need to determine who was the first female detective employed by the Pinkerton Detective Agency. I will apply the principles to connect the historical context and notable figures mentioned. - Principle 1: (Historical Agency, First Female Employee, Female Detective) - This principle helps identify the first female detective within the Pinkerton Detective Agency. - Principle 2: (Organization, Historical Role, Notable Individual) - This principle links the Pinkerton Detective Agency to Kate Warne as the first female detective. </think> <search_knowledge> - Query: who was the first female detective employed by the Pinkerton Detective Agency? </search_knowledge> | | Env: <information> Doc 1(Title: ”Kate Warne”) … Doc 2(Title: ”Private investigator”) … Doc 3(Title: ”Kate Warne”) … </information> | | EvolveR: <think> Based on the retrieved documents, Kate Warne is identified as the first female detective employed by the Pinkerton Detective Agency. This aligns with the historical context and the principles applied. - Principle 1: (Historical Agency, First Female Employee, Female Detective) - This principle confirms Kate Warne as the first female detective within the Pinkerton Detective Agency. - Principle 2: (Organization, Historical Role, Notable Individual) - This principle links Kate Warne to her role as the first female detective in the agency’s history. </think> <search_knowledge> - Query: who was the first female detective employed by the Pinkerton Detective Agency? </search_knowledge> | | Env: <information> Doc 1(Title: ”Kate Warne”) … Doc 2(Title: ”Private investigator”) … Doc 3(Title: ”Kate Warne”)… </information> | | EvolveR: <think> Based on the retrieved documents, Kate Warne is confirmed as the first female detective employed by the Pinkerton Detective Agency. The information aligns with both historical documentation and the application of principles regarding notable figures in organizational history. - Principle 1: (Historical Agency, First Female Employee, Female Detective) - This principle confirms Kate Warne as the first female detective within the Pinkerton Detective Agency. - Principle 2: (Organization, Historical Role, Notable Individual) - This principle links Kate Warne to her role as the first female detective in the agency’s history. </think> | | <answer> Kate Warne </answer> | A.5 Limitation and Broader Impact We acknowledge several limitations and broader implications of our work. The efficacy of our self-distillation mechanism is inherently bounded by the capabilities of the agent’s own model; a less capable model may struggle to distill high-quality principles, thus limiting its evolutionary ceiling. Further research across a broader range of tasks, such as embodied interaction or creative generation, is necessary to fully delineate the boundaries and applicability of the EvolveR paradigm. While our curation mechanisms mitigate experience base growth, ensuring computational efficiency for truly lifelong learning agents also remains an open challenge. Looking forward, the broader impact of this paradigm is significant. On the one hand, EvolveR represents a crucial step towards more autonomous and personalized agents. The explicit nature of its distilled principles also offers a promising avenue for improving interpretability and steerability. On the other hand, this autonomy raises critical safety considerations. An agent that evolves its own principles could develop undesirable strategies if not guided by a robust, value-aligned reward function, necessitating further research into alignment techniques for such self-evolving systems.