2510.08049

## A Survey of Process Reward Models: From Outcome Signals to Process Supervisions for Large Language Models Congmin Zheng * 1 , Jiachen Zhu ∗ 1 , Zhuoying Ou ∗ 1 , Yuxiang Chen 2 , Kangning Zhang 1 , Rong Shan Zeyu Zheng 3 , Mengyue Yang 4 , Jianghao Lin 1† , Yong Yu 1 , Weinan Zhang 1 1 Shanghai Jiao Tong University, 2 University College London, 3 Carnegie Mellon University, 4 University of Bristol {desp.zcm,gebro13,zoeouzy23,linjianghao,wnzhang}@sjtu.edu.cn, ## Abstract Although Large Language Models (LLMs) exhibit advanced reasoning ability, conventional alignment remains largely dominated by outcome reward models (ORMs) that judge only final answers. Process Reward Models (PRMs) address this gap by evaluating and guiding reasoning at the step or trajectory level. This survey provides a systematic overview of PRMs through the full loop: how to generate process data , build PRMs , and use PRMs for test-time scaling and reinforcement learning. We summarize applications across math, code, text, multimodal reasoning, robotics, and agents, and review emerging benchmarks. Our goal is to clarify design spaces, reveal open challenges, and guide future research toward fine-grained, robust reasoning alignment. ## 1 Introduction The advent of Large Language Models (LLMs) has reshaped alignment for reasoning (Shao et al., 2024; Jaech et al., 2024; Yang et al., 2025a; Bai et al., 2025; He et al., 2025a), shifting attention from outcome-only supervision to process-aware evaluation. Early pipelines predominantly relied on outcome reward models (ORMs) (Lightman et al., 2023) that judge only final answers, providing a single coarse signal for long chains of thought. As reasoning tasks grow longer and more complex, this static, outcome-centric view struggles to capture stepwise progress, diagnose intermediate errors, or allocate computation adaptively. To address this gap, the community has begun to move beyond coarse outcome supervision toward process reward models (PRMs), which explicitly assess and guide reasoning at the step or trajectory level. As shown in Figure 1, Process Reward Models coupled with a closed loop: generate process data → train PRMs → use PRMs (test-time * Equal Contribution † Corresponding author Figure 1: The Process Reward Model (PRM) loop that iteratively generates data , trains PRMs , and uses PRMs to improve policies and produce new data. <details> <summary>Image 1 Details</summary>  ### Visual Description \n ## Diagram: PRM Framework Overview ### Overview The image depicts a circular diagram illustrating a framework centered around "PRM" (likely Policy Reinforcement Learning or a similar concept). The diagram is structured as concentric rings, representing different stages or aspects of the framework: Data Process, PRM Training, PRM Usage, and Downstream Application. Each segment within these rings is labeled with specific techniques, data sources, or application areas. The diagram uses icons alongside text labels to visually represent each element. ### Components/Axes The diagram consists of four main concentric rings: 1. **Inner Circle:** Contains the text "PRM" with a gear icon. 2. **Data Process Ring (Purple):** Labeled with "DATA PROCESS" and includes segments for "Human", "Automated", and "Semi-auto". 3. **PRM Training Ring (Green):** Labeled with "PRM TRAINING" and includes segments for "Discriminative", "Generative", "Implicit", and "Architectures". 4. **PRM Usage Ring (Red):** Labeled with "PRM USAGE" and includes segments for "Test-Time Scaling", and "RL for Policy Learning". 5. **Downstream Application Ring (Yellow):** Labeled with "DOWNSTREAM APPLICATION" and includes segments for "Robotics", "Multimodal", "Industry", "Math", "Text", "Code", and "Agent". Arrows indicate a flow from the Data Process ring, through PRM Training and PRM Usage, towards Downstream Application. Each segment also has a corresponding icon. ### Detailed Analysis / Content Details Here's a breakdown of each segment with its associated icon: * **PRM (Center):** Gear icon. * **Data Process:** * **Human:** Icon of a person. * **Automated:** Icon of a robot. * **Semi-auto:** Icon of a hand interacting with a robotic arm. * **PRM Training:** * **Discriminative:** Icon of a magnifying glass. * **Generative:** Icon of a lightbulb. * **Implicit:** Icon of a brain. * **Architectures:** Icon of building blocks. * **PRM Usage:** * **Test-Time Scaling:** Icon of a scale. * **RL for Policy Learning:** Icon of a graph. * **Downstream Application:** * **Robotics:** Icon of a robot head. * **Multimodal:** Icon of multiple screens. * **Industry:** Icon of a factory. * **Math:** Icon of mathematical symbols. * **Text:** Icon of a document. * **Code:** Icon of code brackets. * **Agent:** Icon of a person. The arrows indicate a cyclical flow, suggesting that the PRM framework is iterative. The "PRM Usage" ring has a curved arrow pointing towards the "Downstream Application" ring. ### Key Observations The diagram emphasizes the interconnectedness of data processing, PRM training, PRM usage, and downstream applications. The cyclical nature of the diagram suggests a continuous improvement loop. The variety of downstream applications indicates the broad applicability of the PRM framework. The icons provide a quick visual understanding of each segment. ### Interpretation This diagram illustrates a comprehensive framework for Policy Reinforcement Learning (PRM). It highlights the importance of a robust data process (involving human, automated, and semi-automated methods) as the foundation for effective PRM training. The training phase encompasses both discriminative and generative approaches, leveraging implicit knowledge and architectural design. The trained PRM is then utilized for tasks like test-time scaling and reinforcement learning for policy optimization. Finally, the framework is applied to a diverse range of downstream applications, including robotics, multimodal systems, industry automation, mathematical modeling, text processing, code generation, and agent-based systems. The cyclical flow suggests that insights gained from downstream applications can be fed back into the data process and PRM training, leading to continuous refinement and improvement of the framework. The diagram is a high-level overview and does not provide specific details about the algorithms or techniques used within each segment. It serves as a conceptual map for understanding the overall structure and relationships within the PRM ecosystem. </details> scaling or RL) → produce better data . This loop transforms reward modeling from a one-shot verdict to an iterative controller of reasoning, enabling finer credit assignment, richer diagnostics, and improved robustness. The emergence of PRMs marks a pivotal shift. Rather than relying on single-turn or rule-based evaluation, PRMs assess partial solutions and trajectories, leverage context for adaptive 'reasonthen-rate' verification, and integrate with inferencetime controllers and reinforcement learning (RL) objectives. In this paradigm, supervision becomes proactive: it not only evaluates but also steers search, reflection, and policy updates across diverse sources of evidence (e.g., retrieved knowledge, programs, or multimodal inputs). Given these rapid advances, we present a systematic survey of PRMs across the full loop: how to generate data , how to build PRMs , and how to use PRMs . Current discussions mainly focus on either test-time scaling paradigms (Zhang et al., 2025f), broad reward modeling taxonomies (Zhong et al., 2025), or generic deep RL reward design (Yu et al., 2025), whereas our PRM survey uniquely targets step-level process reward modeling by organizing the full loop of data generation, PRM building, and usage (test-time scaling and PRM-guided RL) for fine-grained reasoning supervision. Specifically, this paper is structured as follows. Sec. 2 ( How to Generate Data ) categorizes process supervision into human annotation , automated supervision , and semi-automated pipelines , highlighting fidelity-scalability trade-offs. Sec. 3 ( How to Build PRMs ) reviews modeling paradigms, including discriminative vs. generative objectives, explicit vs. implicit supervision, and architectural innovations. Sec. 4 ( How to Use PRMs ) discusses test-time scaling (re-ranking, verification-guided decoding, search) and PRM-guided RL (dense stepwise rewards and credit assignment). Sec. 5 includes applications spanning math, code, multimodal reasoning, agents, and high-stakes domains, and Sec. 6 summarizes benchmarks . Further discussions are provided in Sec. 7. ## 2 How to Generate Data In this section, we address the question of "how to generate data" for training process reward models (PRMs) and categorize existing approaches into three main paradigms: (1) human annotation, (2) automated supervision, and (3) hybrid methods that combine both. Each paradigm reflects a different trade-off between fidelity and scalability, and recent work often integrates multiple strategies to leverage the strengths of one source while mitigating the weaknesses of another. ## 2.1 Human Annotation The earliest and most straightforward form of process supervision comes from direct human annotation, where annotators explicitly verify the correctness of intermediate reasoning steps. PRM800K (Lightman et al., 2023) is a representative example, in which human labelers carefully validated each step of multi-hop reasoning chains. This dataset demonstrated that explicitly capturing human judgments about process correctness can substantially improve PRM training, leading to better alignment and more interpretable reasoning outcomes. Although resource-intensive and limited in scale, human-curated process data has proven to be a critical foundation: it provides high-fidelity signals, establishes benchmarks for other data generation pipelines, and often serves as seed material to guide more scalable methods. ## 2.2 Automated Supervision To overcome the bottlenecks of manual labeling, a large body of research explores fully automated approaches that generate process supervision through symbolic verification, consistency checks, execution feedback, or synthetic self-evolution. Math-Shepherd (Wang et al., 2023) introduced an automated verification pipeline where mathematical reasoning steps are validated using symbolic tools and consistency-checking heuristics, enabling large-scale process supervision without human annotations. FOVER (Kamoi et al., 2025) uses formal verification tools (e.g., Z3, Isabelle) to automatically generate PRM training data with accurate step-level error labels. OmegaPRM (Luo et al., 2024) extends this paradigm by using a divide-and-conquer style Monte Carlo Tree Search (MCTS) algorithm to efficiently identify the first error in a reasoning chain, providing a scalable alternative to human judgment. URSA (Luo et al., 2025) further advances this line by synthesizing process-level supervision for multimodal mathematical reasoning through a fully automated dualview pipeline, which employs MCTS-based error localization and misinterpretation insertion engines to construct large-scale process annotations. Expanding beyond mathematics, MTRewardTree (Feng et al., 2025b) adapts the MCTS-driven framework to machine translation, leveraging approximate MCTS to generate tokenlevel preference pairs entirely through automatic evaluation and filtering, thereby enabling scalable and fine-grained reward modeling without human annotation. Similarly, CodePRM (Li et al., 2025a) employs automated tree search and execution feedback to derive step-level supervision for code reasoning, achieving fully automatic label generation without human involvement. Searchin-Context (Chen et al., 2025d) introduces Monte Carlo Tree Search with dynamic retrieval, which automatically constructs intermediate reasoning steps without requiring human-annotated reasoning chains or task-specific rewards. Some approaches take automation even further. In AlphaMath (Chen et al., 2024), researchers propose an even more radical approach: deriving pseudo-process supervision directly from outcome supervision, thereby eliminating the need for stepwise labels altogether. More structured methods have also been developed, such as TreePLV (He et al., 2024), which learns preferences over trees of reasoning trajectories automatically constructed via a best-first search algorithm. Building on this trend, rStar-Math (Guan et al., 2025) and Qwen2.5-Math PRM (Zhang et al., 2025j) adopt self-evolutionary and consensus-filtering strategies respectively to create massive reasoning datasets, while EpicPRM (Sun et al., 2025b) focuses on balancing precision and scale in constructing processsupervised training data. To improve robustness, SCAN (Ding et al., 2025) introduces a self-denoising annotation framework that automatically detects and corrects noisy labels, and Wang et al. (2025c) proposes a data augmentation strategy based on node merging in the tree structure. Collectively, these works showcase the promise of automated pipelines: they enable unprecedented scale and efficiency, though they must carefully address error propagation, verifier limitations, and potential misalignment with human reasoning preferences. ## 2.3 Semi-automated Approaches Between these two extremes, a growing number of works adopt semi-automated approaches, blending selective human input with scalable automated expansion. In multimodal reasoning, this pattern is especially pronounced: VRPRM (Chen et al., 2025f) and Athena (Wang et al., 2025b) both construct PRM datasets by starting with limited humancurated reasoning steps and then expanding them with automated verification or synthetic generation, significantly improving data efficiency. ViLBench (Tu et al., 2025) and VisualPRM (Wang et al., 2025f) adopt similar strategies in visionlanguage reasoning, mixing curated samples with large-scale synthetic data to create comprehensive benchmarks. In more specialized domains, MedS 3 (Jiang et al., 2025) adopts a self-evolved 'slow thinking' paradigm for medical reasoning: it starts from around 8,000 human-curated examples and then automatically expands them via MCTS-based exploration and rule-verifiable trajectory generation, greatly reducing manual workload while retaining domain reliability. Beyond single-domain settings, VersaPRM (Zeng et al., 2025) generates synthetic reasoning data across multiple domains primarily via auto-labeling, with a small-scale manual evaluation conducted to verify the quality of the auto- labeled data. Practical task-oriented applications also rely on hybrid pipelines. Web-Shepherd (Chae et al., 2025) supervises web navigation reasoning traces by mixing human oversight with automatic checks, while GUI-Shepherd (Chen et al., 2025a) builds the PRM dataset via a dual-pipeline strategy combining diverse trajectories with hybrid human-GPT annotations. Finally, ActPRM (Duan et al., 2025) exemplifies active learning in PRM training, selectively querying human annotators only when automated signals are uncertain, thereby reducing labeling costs without sacrificing supervision quality. These hybrid methods illustrate that carefully combining human anchors with automated pipelines not only mitigates the weaknesses of each approach but also opens up broader applications in domains where neither purely human nor purely automated supervision is sufficient. ## 3 How to Build PRMs In this section, we answer the question of "how to build PRMs" and categorize PRM training works into four classes: Discriminative PRMs, Generative PRMs, Implicit PRMs, and Other Architectures. Furthermore, we provide detailed discussions of representative methods in each category. ## 3.1 Discriminative PRMs. A discriminative PRM learns a scoring function over intermediate reasoning states to predict perstep correctness, plausibility, or progress. Given an input x and a partial solution s 1: t , the model outputs a scalar score as Eq. 1 shows. <!-- formula-not-decoded --> Pointwise loss. The score r t can be trained with standard pointwise objectives. Here σ is the sigmoid function, and f θ denotes the discriminative PRMs. With binary labels y t ∈ { 0 , 1 } or soft labels y t ∈ [0 , 1] , one typically uses either binary crossentropy (BCE) or mean squared error (MSE): <!-- formula-not-decoded --> <!-- formula-not-decoded --> Pairwise (preference) loss. Alternatively, discriminative PRMs can be trained on relative preferences between two candidate steps or partial traces u and v . The model predicts the probability that u is preferred to v : <!-- formula-not-decoded --> and minimizes a pairwise (preference) loss such as: <!-- formula-not-decoded --> which is analogous to the Direct Preference Optimization (DPO) objective used in RLHF. Discriminative PRMs, viewed as the foundational training paradigm in the history of processlevel reward models, have inspired lots of works. DreamPRM (Cao et al., 2025b) alternately trains the PRM and domain weights through a bi-level strategy to generalize across multimodal tasks; PQM (Li and Li, 2024) recasts PRM as a Qvalue ranking problem, aligning rewards by relative ordering; ER-PRM (Zhang et al., 2024) injects entropy regularization into the reward objective to avoid overconfident predictions and improve calibration; EDU-PRM (Cao et al., 2025a) uses entropy-based uncertainty sampling and weighting to focus training on ambiguous or difficult reasoning steps; Q-RM (Chen et al., 2025b) introduces token-level discriminative loss to provide finer-grained feedback on intermediate tokens; BiPRM (Zhang et al., 2025d) seamlessly integrates a parallel right-to-left (R2L) evaluation stream with the conventional L2R flow, allowing later reasoning steps to real-time assist in assessing earlier ones;RPRM (She et al., 2025) designs a loss function that favors logical and structural consistency across reasoning steps; BiRM (Chen et al., 2025e) not only evaluates the correctness of previous steps, but also models the probability of future success; CoLD(Zheng et al., 2025) uses counterfactual guidance to mitigate length bias in reward scoring; and ProgRM (Zhang et al., 2025a) defines dynamic 'progress rewards' that proportionally align process rewards with the degree of task completion. ## 3.2 Generative PRMs. A generative PRM operates in two stages: it first generates a verification or critique chain z t ('think'), and then judges or scores the original reasoning step based on that chain ('judge'). Concretely, one can write: <!-- formula-not-decoded --> where p ϕ is the generative verifier or critic model, and h ψ is a scoring head that maps the generated chain and the step history to a step-level reward r t . A plausible joint training objective combines a likelihood loss for the verification chain and a supervision term for the step-level reward: <!-- formula-not-decoded --> where z ⋆ t is a reference (e.g., human or oracle) critique chain, and y t is the ground-truth (or soft) label for the step. In many works, h ψ is simply the confidence of the answer logits. Assume token indices k yes and k no correspond to 'yes' and 'no' respectively. Then define r t as the softmax score: <!-- formula-not-decoded --> This generative PRM paradigm helps the reward model maintain long reasoning chains (i.e., extended 'thinking') and better understand the semantics of the input. ThinkPRM (Lee et al., 2025) uses an internal 'thinking' loop to simulate generative reflection and enable dynamic reasoning. GenRM (Zhang et al., 2025e) introduces chain-ofthought at inference and uses voting to pick the highest-scoring reasoning chain to improve consistency. GenPRM (Zhao et al., 2025) applies generative computation scaling at test time to boost the stability of reward predictions. GRAM-R² (Wang et al., 2025a) self-trains a generative foundation reward model that evolves its own reasoning and reward logic. Process-based Self-Rewarding Language Models (Zhang et al., 2025g) allow the model to both generate and assess its own reasoning chains, closing the loop between reasoning and reward. Test-Time Scaling with Reflective Generative Model (Wang et al., 2025g) expands inferencetime generative capacity and applies reflection to refine reward prediction. GM-PRM (Zhang et al., 2025b) is the first multimodal generative PRM, supporting chain generation in multimodal mathematical reasoning tasks. rStar-Math (Guan et al., 2025) strengthens smaller models' reasoning by evolving deep thinking through self-evolution in its internal reasoning architecture. ## 3.3 Implicit PRMs The above discriminative and generative PRM methods all rely on explicit supervision signals derived from annotated reasoning steps; in contrast, implicit PRMs aim to infer fine-grained rewards without step-level labels, by leveraging weaker or indirect supervision such as outcome feedback, model self-evaluation, or consistency constraints. Implicit PRM extracts step rewards from unlabeled trajectories; FreePRM (Sun et al., 2025a) trains a reward model without ground-truth process labels by pseudo-labeling via outcome correctness; SelfPRM (Feng et al., 2025a) shows that LLMs under RL training can internally induce a PRM-style selfrewarding capability; SP-PRM (Xie et al., 2025a) transfers reasoning knowledge from an outcome reward model (ORM) into process reward modeling to reduce label dependency; SPARE (Rizvi et al., 2025) uses one-shot reference guidance to automatically generate supervision signals for intermediate steps; Universal PRM (AURORA) (Tan et al., 2025) employs ensemble prompting and reverse verification to produce domain-agnostic selfsupervised reward signals; and Process-based SelfRewarding Language Models let the model generate and evaluate its own reasoning chain, closing the loop for self-supervision. ## 3.4 Other Architectural Innovations Other architectures in the PRM landscape emphasize innovations in model structure, reasoning representations, or system frameworks rather than new loss functions or supervision schemes. For example, GraphPRM (Peng et al., 2025) casts reasoning as a graph of steps and learns structured dependencies among them; ASPRM (AdaptiveStep) (Liu et al., 2025) dynamically adjusts the granularity of reasoning steps based on model confidence; Reward-SQL (Zhang et al., 2025i) builds a structured process reward model tailored to the Text-toSQL domain; RetrievalPRM (Zhu et al., 2025) integrates external retrieval to ground reward predictions and improve cross-task generalization; OpenPRM (Zhang et al., 2025c) organizes reward judgments into an open preference tree, supporting branching and domain flexibility; MM-PRM (Du et al., 2025) provides a unified multimodal PRM architecture and open implementation; Multilingual PRM (Wang et al., 2025e) addresses crosslanguage CoT transfer through representational mapping across languages; PathFinder-PRM (Pala et al., 2025a) employs a hierarchical error-aware architecture to distinguish and reward different types of reasoning errors; and Hierarchical Reward Model (HRM) (Wang et al., 2025d) proposes lay- ered reward structures aligned with multi-level reasoning abstractions. ## 4 How to Use PRMs In this section, we discuss how to use PRMs and organize their usage into two main paradigms: Test-Time Scaling and Reinforcement Learning for Policy Learning. We further provide detailed discussions of representative methods and developments within each paradigm, highlighting how PRMs guide inference, search, and policy learning through fine-grained step-level feedback. ## 4.1 Test-Time Scaling Test-time scaling aims to improve model performance not by enlarging model size but by strategically allocating computation during inference-via candidate sampling, re-ranking, or guided search. PRMs are central to this process, providing finegrained evaluation of intermediate reasoning steps and trajectories to guide test-time computation. Early work used PRMs primarily as re-rankers. Studies such as Lightman et al. (2023); Wang et al. (2023, 2025f,b); Zheng et al. (2025) showed that Best-of-N re-ranking with PRM scores consistently improves final performance, validating PRMs as reliable test-time evaluators. Building on this foundation, PRMs evolved into generative verifiers. GenPRM (Zhao et al., 2025) introduced verificationby-generation, producing reasoning or code checks before scoring candidates. ThinkPRM (Snell et al., 2024) fine-tunes long chain-of-thought verifiers with limited process-level labels, enhancing scaling under Best-of-N and beam search. Kim et al. (2025) formalized reasoning-oriented evaluation as a mechanism for allocating test-time compute more effectively, positioning PRMs as flexible controllers of inference resources. Parallel efforts integrated PRMs into search and decoding algorithms. PRM-BAS (Hu et al., 2025a) embedded PRMs into beam annealing search, pruning low-quality candidates to improve efficiency. CodePRM (Li et al., 2025a) implemented a Generate-Verify-Refine pipeline, using PRMs to detect and correct faulty intermediate code steps. Web-Shepherd (Chae et al., 2025) filtered webagent trajectories, while other approaches combined PRMs with MCTS or retrieval-augmented reasoning (Chan et al., 2025; Ma et al., 2025; Chen et al., 2025d). Safety-aware scaling was addressed by SAFFRON-1, which reduced costly PRM calls and introduced caching mechanisms to ensure robust, efficient inference under adversarial conditions. Finally, refinements targeted step-level granularity and adaptivity. AdaptiveStep (Liu et al., 2025) dynamically partitions reasoning into finer steps based on confidence, producing sharper PRM judgments. SP-PRM (Xie et al., 2025b) extended reward-guided search strategies across multiple granularity levels, from tokens to full responses, enhancing both precision and flexibility. Together, these developments trace a clear trajectory: from static PRM-based re-ranking, through generative verification and search integration, to adaptive step-level refinements and safety-aware scaling, transforming PRMs into dynamic, scalable controllers of inference. ## 4.2 RL for Policy Learning The use of process reward models (PRMs) within reinforcement learning (RL) has become a promising direction for aligning language models with fine-grained reasoning quality. Traditional RL relies on outcome-only supervision, which is sparse and often misaligned with intermediate reasoning steps. By contrast, PRMs provide dense step-level or trajectory-level feedback that can be integrated into RL training loops, offering more stable credit assignment and faster policy learning. Early explorations established that PRMs could directly replace sparse correctness-based signals with fine-grained supervision during RL. MathShepherd (Wang et al., 2023) trained an automatic verifier that scores each intermediate step in math reasoning and used those scores as rewards for PPO, allowing the policy to learn from abundant intermediate feedback when final answers are rare. In a similar vein, Dai et al. (2024) demonstrated how line-level PRM signals could be injected into RL training, overcoming the limitations of outcome-only feedback from unit tests and enabling policies to improve across long coding trajectories. Extending this idea to practical domains, Reward-SQL (Zhang et al., 2025i) integrated stepwise PRMs into an online RL loop, showing that process-level signals are especially valuable in text-to-SQL generation, while ReasonRAG (Zhang et al., 2025g) applied PRM-guided RL to retrieval-augmented generation agents. Together, these works show that PRMs can serve as actionable dense rewards that significantly improve RL training across reasoning-heavy tasks. Building on this foundation, several studies refined the formulation of PRM signals within RL objectives. PA V (Setlur et al., 2024) reframed steplevel PRM outputs as advantage-like progress indicators, providing dense step-level rewards for RL training of policy models. ER-PRM (Zhang et al., 2024) introduced an entropy-regularized framework that embeds PRM rewards into KLconstrained RL objectives, stabilizing training while preserving exploration. PURE (Cheng et al., 2025) addressed a fundamental credit-assignment challenge, arguing that summing PRM rewards encourages reward hacking and instead proposing a min-form objective that integrates PRM signals into RL updates more robustly. Q-RM (Chen et al., 2025c) advanced token-level supervision by modeling Q-values over tokens and using them directly as rewards during RL optimization. CAPO (Xie et al., 2025c) introduces verifiable generative credit assignment to produce reliable step-level rewards for RL training of policy models. These verifiable rewards replace sparse outcome signals, improving exploration and sample efficiency. These innovations highlight that beyond having PRM feedback, the way PRM outputs are incorporated into RL loss functions critically affects training stability and effectiveness. He et al. (2025b) introduces a generative, thought-level PRM that assigns reliable grouped step-level rewards for RL training of policy models integrating with an off-policy algorithm and adaptive reward balancing. Meanwhile, PROF (Ye et al., 2025) ranks and filters responses based on process-outcome consistency between PRMs and ORMs, removing samples where reasoning and results conflict to reduce noisy gradients. It further maintains balanced training by separately ranking correct and incorrect responses, and can be seamlessly integrated with RL methods such as GRPO (Shao et al., 2024). In parallel, domain-specific efforts such as GraphPRM (Peng et al., 2025) used PRM-guided preference optimization to improve reasoning over graph reasoning problems, while AgentPRM (Choudhury, 2025) integrated PRMs into an actor-critic loop for LLM-based agents, showing how step-level critics can accelerate RL in interactive settings. These results demonstrate that PRMs can make RL training more robust across diverse reasoning tasks. Broader frameworks have emerged to consolidate and scale these practices. OpenR (Wang et al., 2024) provides an open-source infrastructure that systematizes the integration of PRMs into both offline and online RL pipelines, offering recipes for PRM-guided training across reasoning benchmarks. ## 5 Downstream Application Process Reward Models (PRMs) are increasingly adopted across diverse reasoning and decisionmaking tasks. Below we summarize representative application areas. Math PRMs validate algebraic and logical steps to ensure multi-step derivation soundness (Zhou et al., 2025a; Uesato et al., 2022; Lightman et al., 2023; Wang et al., 2023), capturing symbolic and arithmetic errors to improve final correctness (Li et al., 2024; He et al., 2024; Pala et al., 2025b). They support scalable supervision and automated feedback for grading, tutoring, and proof validation with reduced human effort (Chen et al., 2024; Setlur et al., 2024; Zhao et al., 2025; Sun et al., 2025b). Code For code generation, PRMs assess partial programs with execution or proxy testing feedback (Li et al., 2025a; Dai et al., 2024), rewarding syntactic validity and semantic consistency. They also verify query construction and patches in textto-SQL and software engineering (Zhang et al., 2025g; Gandhi et al., 2025), improving robustness. Multimodal In multimodal reasoning, PRMs check visual-text coherence (Hu et al., 2025a; Du et al., 2025; Chen et al., 2025f; Tu et al., 2025; Wang et al., 2025f), rerank reasoning traces, and select grounded explanations to enhance interpretability and factual consistency. Text For text tasks, PRMs refine multi-step reasoning by evaluating partial translations (Feng et al., 2025b) and scoring intermediate hops in QAand retrieval-augmented reasoning (Chan et al., 2025; Chen et al., 2025d), improving coherence and factual reliability. Robotics PRMs decompose long-horizon manipulation or navigation into subgoal rewards (Lu et al., 2025), providing dense feedback that accelerates policy learning and stabilizes control. Agents In interactive agents, PRMs act as trajectory critics (Choudhury, 2025; Hu et al., 2025b; Chae et al., 2025; Zhang et al., 2025h,a; Chen et al., 2025a; Xi et al., 2025; Yang et al., 2025c), rewarding meaningful progress, pruning dead ends, and improving safety during inference. Industry In high-stakes areas like medicine and finance, PRMs enforce verifiable, evidence-based reasoning (Jiang et al., 2025; Zhou et al., 2025b), promoting reliability and risk-sensitive decision making. Multi-domain Recent studies explore generalizable PRMs that transfer process supervision across tasks (Cao et al., 2025b; Wang et al., 2025b; Zhang et al., 2025c; Zeng et al., 2025; Rizvi et al., 2025; Xie et al., 2025b; Ding et al., 2025; Tan et al., 2025), pointing toward universal, cross-domain reasoning evaluators. ## 6 Benchmark Recent work has introduced a range of benchmarks to evaluate PRMs at the step level, differing in scale, domain, and evaluation focus. For mathematical reasoning, PRMBench (Song et al., 2025) and ProcessBench (Zheng et al., 2024) offer complementary views. PRMBench provides over 6,000 problems with 80,000 step annotations and multidimensional labels (e.g., simplicity, soundness, sensitivity), while ProcessBench targets competition-level tasks, emphasizing earliest-error detection for precise symbolic reasoning. Reasoning-structure evaluation is addressed by Socratic-PRMBench (Li et al., 2025b), which groups nearly three thousand flawed trajectories into six error patterns, enabling analysis of generalization across reasoning styles. For multimodal tasks, ViLBench (Tu et al., 2025) compares PRMs with outcome models in visionlanguage reasoning, VisualProcessBench (Wang et al., 2025f) provides human-labeled multimodal errors, and MPBench (Xu et al., 2025) extends coverage to multiple tasks, assessing step correctness, answer aggregation, and reasoning-guided search. Long-horizon decision-making is tested by WebRewardBench (Chae et al., 2025), built on the WebPRM Collection with forty thousand step-level preference pairs, evaluating clicks, form entries, and navigation steps in web agents. Robustness and universality are explored by GSM-DC (Yang et al., 2025b), which injects distractors to test resilience, and UniversalBench (Tan et al., 2025), which evaluates trajectories across diverse policy distributions for cross-distribution generalization and reproducibility. Figure 2: Comparative Analysis of Three Reward Mechanisms Across Six Evaluation Aspects <details> <summary>Image 2 Details</summary>  ### Visual Description \n ## Radar Chart: Multi-Criteria Performance Comparison ### Overview The image presents a radar chart (also known as a spider chart or star chart) comparing the performance of three different reward models – Rule-Based Reward, Outcome Reward Model, and Process Reward Model – across six criteria: Resource Efficiency (RE), Granularity (Gr), Anti-Hacking robustness (AH), Generalization (Ge), Interpretability (In), and Functionality (Fu). The chart uses a star-shaped polygon to visualize the performance of each model, with each vertex representing a criterion. The further a point is from the center, the higher the performance on that criterion. ### Components/Axes * **Axes/Criteria:** The six criteria are arranged radially around the center of the chart. They are: * Resource Efficiency (RE) - Yellow * Granularity (Gr) - Purple * Anti-Hacking robustness (AH) - Pink * Generalization (Ge) - Teal * Interpretability (In) - Blue * Functionality (Fu) - Red * **Legend:** Located in the top-right corner and bottom-right corner of the image, the legend maps colors to reward models: * Green Dashed Line: Rule-Based Reward * Blue Dashed Line: Outcome Reward Model * Navy Blue Dotted Line: Process Reward Model * **Radial Scale:** Concentric circles indicate performance levels: Low, Moderate, High. * **Icons:** Small icons are placed near each criterion label, visually representing the concept. ### Detailed Analysis The chart displays three polygonal lines, each representing a reward model. * **Rule-Based Reward (Green Dashed Line):** * RE: Approximately High (near the outer circle). * Gr: Approximately Moderate (midway between the center and outer circle). * AH: Approximately Low (close to the center). * Ge: Approximately Moderate (midway between the center and outer circle). * In: Approximately High (near the outer circle). * Fu: Approximately Moderate (midway between the center and outer circle). * Trend: The line fluctuates, showing strong performance in RE and In, but weaker performance in AH. * **Outcome Reward Model (Blue Dashed Line):** * RE: Approximately Low (close to the center). * Gr: Approximately High (near the outer circle). * AH: Approximately Moderate (midway between the center and outer circle). * Ge: Approximately Low (close to the center). * In: Approximately Moderate (midway between the center and outer circle). * Fu: Approximately Moderate (midway between the center and outer circle). * Trend: The line shows a peak in Gr, but generally remains closer to the center, indicating lower overall performance. * **Process Reward Model (Navy Blue Dotted Line):** * RE: Approximately Moderate (midway between the center and outer circle). * Gr: Approximately Moderate (midway between the center and outer circle). * AH: Approximately High (near the outer circle). * Ge: Approximately Moderate (midway between the center and outer circle). * In: Approximately Low (close to the center). * Fu: Approximately Moderate (midway between the center and outer circle). * Trend: The line shows a peak in AH, but lower performance in In. ### Key Observations * The Rule-Based Reward model excels in Resource Efficiency and Interpretability. * The Outcome Reward Model is strongest in Granularity. * The Process Reward Model demonstrates the highest Anti-Hacking robustness. * No single model dominates across all criteria; each has its strengths and weaknesses. * Interpretability (In) is a relative weakness for the Process Reward Model. * Generalization (Ge) is a relative weakness for the Outcome Reward Model. ### Interpretation This radar chart provides a comparative assessment of three reward models based on six key performance indicators. The visualization highlights the trade-offs inherent in each model. For example, the Rule-Based Reward model offers high Resource Efficiency and Interpretability but is vulnerable to hacking. Conversely, the Process Reward Model prioritizes security (Anti-Hacking robustness) at the expense of Interpretability. The chart suggests that the optimal choice of reward model depends on the specific application and the relative importance of each criterion. If security is paramount, the Process Reward Model is the best choice. If resource efficiency and ease of understanding are critical, the Rule-Based Reward model is preferable. The Outcome Reward Model is best suited for applications where fine-grained control (Granularity) is essential. The use of a radar chart effectively communicates these complex relationships, allowing for a quick and intuitive understanding of the strengths and weaknesses of each model. The concentric circles provide a clear visual indication of performance levels, and the color-coding facilitates easy comparison. </details> ## 7 Discussion To better compare the different forms of reward acquisition, including rule-based rewards, outcome reward models (ORMs), and process reward models (PRMs), we design a six-aspect evaluation scheme covering resource efficiency, granularity, anti-hacking robustness, generalization, interpretability, and functionality. This perspective provides a systematic and balanced basis for assessing how each reward mechanism performs across theoretical soundness, practical applicability, and scalability, as illustrated in Figure 2. From the perspective of resource efficiency, rulebased rewards stand out as the most economical approach, since they rely purely on manually defined rules without the need for additional data labeling or model training. ORMs require moderate resources, as they depend only on final outcome labels and a single-stage model training process. In contrast, PRMs demand relatively higher resources due to the necessity of step-wise annotations and more complex training pipelines, though their higher annotation cost can often be justified by the richer supervision signal they provide. In terms of granularity, rule-based systems offer moderate flexibility, as the level of reward detail can be adjusted by designing more or fewer rules. ORMs, however, operate at a coarse granularity because they assign rewards solely based on the correctness of the final outcome, ignoring the intermediate reasoning process. PRMs provide the finest granularity, delivering step-wise evaluations that enable more nuanced and interpretable feedback during reasoning or decision-making. Regarding anti-hacking robustness, ORMs exhibit the strongest resistance to reward hacking. Since the reward signal is tied directly to the cor- rectness of the final output. In contrast, rule-based rewards are prone to exploitation if the predefined rules are incomplete or mis-specified, leading to unintended optimization behaviors. PRMs lie between the two; while their process-level supervision provides more structure, they remain susceptible to biases in step-level annotations and to overfitting human-preference artifacts. In the dimension of generalization, ORMs show a clear advantage. Their outcome-centric formulation allows the same evaluation principle to be easily transferred across tasks and domains. Rulebased systems, by contrast, exhibit poor generalization, as their rules must be carefully re-engineered for each new environment. PRMs demonstrate moderate generalization capacity, since the idea of evaluating intermediate reasoning steps can generalize across domains, but the specific reward model often requires re-adaptation to the reasoning style or structure of the new task. From the standpoint of interpretability, rulebased rewards are inherently transparent, as their logic and intent are explicitly encoded in the rules themselves. ORMs, on the other hand, suffer from low interpretability, offering only a final judgment with little insight into why an outcome is deemed correct or incorrect. PRMs occupy a middle ground; their step-wise supervision offers richer information than ORMs, yet their learned representations and internal scoring mechanisms may still lack full interpretability. Finally, in terms of functionality, PRMs are the most versatile. Their step-wise feedback can be seamlessly integrated into a wide range of reinforcement learning and Test-time Scaling frameworks, enabling fine-grained optimization and guided reasoning. ORMs possess moderate functionality; they can be applied in multiple tasks involving final outcome evaluation but are limited in their ability to guide intermediate reasoning. Rulebased rewards, while straightforward, are functionally restricted, as they lack adaptability and additional utility beyond the scenarios for which the rules were originally designed. ## 8 Conclusion Process Reward Models (PRMs) shift reasoning alignment from coarse outcome judgments to finegrained, step-level feedback, forming a closed loop of data generation , model training , and usage that continually improves reasoning quality. Our survey organizes this field around how to generate process data, build PRMs, and use them for test-time scaling and reinforcement learning, while summarizing benchmarks and applications across math, code, multimodal tasks, robotics, and other domains. Key challenges ahead include reducing annotation cost via robust automatic supervision, improving cross-domain generalization, integrating PRMs with agentic planning and memory, and establishing standardized evaluation protocols. Addressing these will advance safer, more interpretable, and broadly applicable reasoning systems. ## 9 Limitations While this survey aims to provide a broad and systematic view of Process Reward Models (PRMs), it also has several natural limitations. First, our taxonomy follows the data-model-usage loop and thus simplifies or abstracts some hybrid methods; certain approaches may span multiple categories and are discussed only under their primary aspect. 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Wenlin Zhang, Xiangyang Li, Kuicai Dong, Yichao Wang, Pengyue Jia, Xiaopeng Li, Yingyi Zhang, Derong Xu, Zhaocheng Du, Huifeng Guo, and 1 others. 2025h. Process vs. outcome reward: Which is better for agentic rag reinforcement learning. arXiv preprint arXiv:2505.14069 . Yuxin Zhang, Meihao Fan, Ju Fan, Mingyang Yi, Yuyu Luo, Jian Tan, and Guoliang Li. 2025i. Rewardsql: Boosting text-to-sql via stepwise reasoning and process-supervised rewards. arXiv preprint arXiv:2505.04671 . Zhenru Zhang, Chujie Zheng, Yangzhen Wu, Beichen Zhang, Runji Lin, Bowen Yu, Dayiheng Liu, Jingren Zhou, and Junyang Lin. 2025j. The lessons of developing process reward models in mathematical reasoning. arXiv preprint arXiv:2501.07301 . Jian Zhao, Runze Liu, Kaiyan Zhang, Zhimu Zhou, Junqi Gao, Dong Li, Jiafei Lyu, Zhouyi Qian, Biqing Qi, Xiu Li, and 1 others. 2025. Genprm: Scaling test-time compute of process reward models via generative reasoning. arXiv preprint arXiv:2504.00891 . Chujie Zheng, Zhenru Zhang, Beichen Zhang, Runji Lin, Keming Lu, Bowen Yu, Dayiheng Liu, Jingren Zhou, and Junyang Lin. 2024. Processbench: Identifying process errors in mathematical reasoning. arXiv preprint arXiv:2412.06559 . Congmin Zheng, Jiachen Zhu, Jianghao Lin, Xinyi Dai, Yong Yu, Weinan Zhang, and Mengyue Yang. 2025. Cold: Counterfactually-guided length debiasing for process reward models. arXiv preprint arXiv:2507.15698 . Jialun Zhong, Wei Shen, Yanzeng Li, Songyang Gao, Hua Lu, Yicheng Chen, Yang Zhang, Wei Zhou, Jinjie Gu, and Lei Zou. 2025. A comprehensive survey of reward models: Taxonomy, applications, challenges, and future. arXiv preprint arXiv:2504.12328 . Chenyu Zhou, Tianyi Xu, Jianghao Lin, and Dongdong Ge. 2025a. Steporlm: A self-evolving framework with generative process supervision for operations research language models. arXiv preprint arXiv:2509.22558 . Yuanchen Zhou, Shuo Jiang, Jie Zhu, Junhui Li, Lifan Guo, Feng Chen, and Chi Zhang. 2025b. Fin-prm: A domain-specialized process reward model for financial reasoning in large language models. arXiv preprint arXiv:2508.15202 . Jiachen Zhu, Congmin Zheng, Jianghao Lin, Kounianhua Du, Ying Wen, Yong Yu, Jun Wang, and Weinan Zhang. 2025. Retrieval-augmented process reward model for generalizable mathematical reasoning. arXiv preprint arXiv:2502.14361 . ## A Paper Structure and Taxonomy Overview Figure 3 illustrates the organizational structure and taxonomy adopted in this survey. At the top level, the survey is built around the full PRM loop: Data Process (Sec. 2), PRM Training (Sec. 3), PRM Usage (Sec. 4), and Benchmark (Sec. 6). Each component is further decomposed into finer categories to reflect the main research threads and representative works. Data Process. We categorize data construction methods into three paradigms: Human Annotation (§2.1), which builds high-fidelity step-level supervision through expert labeling; Automated Supervision (§2.2), which scales data generation with verifiers, search, and synthetic signals; and Semiautomated Approaches (§2.3), which combine limited manual curation with automatic expansion to balance fidelity and scalability. PRMTraining. Modeling methods are grouped into four classes: Discriminative PRMs (§3.1), which directly score step correctness with pointwise or pairwise objectives; Generative PRMs (§3.2), which generate critique or verification chains before rating steps; Implicit PRMs (§ 3.3), which derive rewards without explicit labels via self-supervision or outcome transfer; and Other Architectures (§ 3.4), covering graph-based, retrievalaugmented, multilingual, and specialized structural designs. PRMUsage. We summarize two primary usage paradigms: Test-Time Scaling (§4.1), where PRMs re-rank, verify, and adaptively guide reasoning during inference; and RL for Policy Learning (§4.2), where PRM signals serve as dense rewards for reinforcement learning to improve reasoning policies. Benchmark. The bottom layer highlights major benchmarks (§6) for PRM evaluation, spanning Figure 3: The overall structure of this paper. <details> <summary>Image 3 Details</summary>  ### Visual Description \n ## Diagram: Taxonomy of Prompting Methods for Large Language Models ### Overview This diagram presents a hierarchical taxonomy of prompting methods used for Large Language Models (LLMs). The taxonomy is structured around two main axes: Data Process and Training. Within each axis, methods are categorized into levels of automation (Human Annotation, Automated Supervision, Semi-automated Approaches, and Discriminative/Generative PRMs) and training paradigms (Prompt Training and In-Context Learning). Each node in the tree represents a specific prompting technique, accompanied by citations. The diagram is oriented with the root nodes at the top and branching downwards. ### Components/Axes * **Main Axes:** * Data Process (Top-Left) * Prompt Training (Top-Center) * In-Context Learning (Top-Right) * **Sub-Categories within Data Process:** * Human Annotation (§2.1) * Automated Supervision (§2.2) * Semi-automated Approaches (§2.3) * Discriminative PRMs (§3.1) * **Sub-Categories within Prompt Training:** * Generative PRMs (§3.2) * Discrete PRMs (§3.3) * Continuous PRMs (§3.4) * **Sub-Categories within In-Context Learning:** * Demonstration (§4.1) * Retrieval (§4.2) * Generated Knowledge (§4.3) * **Legend/Citations:** Each method is followed by a citation in the format "Author(s) et al., Year". The section number is also included in parentheses (e.g., §2.1). ### Detailed Analysis or Content Details Here's a breakdown of the methods listed under each category, with approximate counts where applicable. **Data Process:** * **Human Annotation (§2.1):** PRM800K (Lightman et al., 2023) * **Automated Supervision (§2.2):** Math-Shepherd (Wang et al., 2023); FOVER (Kamoi et al., 2025); OmegaProm (Luo et al., 2024); URSA (Luo et al., 2025); MT-RewardTree (Feng et al., 2025b); CodeProm(Li et al., 2025a); SIC (Chen et al., 2025d); AlphaMath (Chen et al., 2024); Tree-PLV (He et al., 2024); rStar-Math (Guan et al., 2025); Zhang et al. (2025c); EpicPRM (Sun et al., 2025b); SCAN (Ding et al., 2025); Wang et al. (2025c). (Approximately 13 methods) * **Semi-automated Approaches (§2.3):** VPRPRM (Chen et al., 2025f); Athena (Wang et al., 2025b); ViLBenc (Tu et al., 2025); VisualPRM (Wang et al., 2025); Meds^3 (Jiang et al., 2025); VersaPRM (Zeng et al., 2025); Web-Shepherd (Chae et al., 2025a); ActPRM (Duan et al., 2025); GUI-Shepherd (Chen et al., 2025a). (Approximately 9 methods) * **Discriminative PRMs (§3.1):** DreamPRM (Cao et al., 2025b); POM (Li and Li, 2024); ER-PRM (Zhang et al., 2024); EDU-PRM (Cao et al., 2025a); Q-RM (Chen et al., 2025b); R-PRM (She et al., 2025); BiPRM (Zhang et al., 2025d); BiRM (Chen et al., 2025e); COLD (Zheng et al., 2025); ProgPRM (Zhang et al., 2025a). (Approximately 10 methods) **Prompt Training:** * **Generative PRMs (§3.2):** ThinkPRM (Lee et al., 2025); GenRM (Zhang et al., 2025e); Gen-PRM (Zhao et al., 2025); GRAMR^2 (Wang et al., 2025a); Process-based Self-Rewarding LMs (Zhang et al., 2025g); Test-Time Scaling with Reflective Generative Model (Guan et al., 2025); GM-PRM (Zhang et al., 2025f); rStar-Math (Wang et al., 2025b). (Approximately 8 methods) * **Discrete PRMs (§3.3):** ZeroDPRM (Sun et al., 2025a); Self-PRM (Park et al., 2025); Prefix-Tuning (Li et al., 2021); AutoPrompt (Shin et al., 2020); GradientPrompt (Pratt et al., 2020); P-Tuning (Lü et al., 2022); Prompt-Tuning (Leskovec et al., 2021). (Approximately 7 methods) * **Continuous PRMs (§3.4):** Reparameterizable Prompt Tuning (Hu et al., 2022); LoRA (Hu et al., 2021); AdaLoRA (Zhang et al., 2024); IA^3 (Mou et al., 2025); BitFit (Zaken et al., 2021); UniPELT (Wang et al., 2025d); PEFT (Mangrulkar et al., 2023). (Approximately 7 methods) **In-Context Learning:** * **Demonstration (§4.1):** Exemplar-PRM (Yang et al., 2025); DemoPRM (Li et al., 2025b); Active-PRM (Zhao et al., 2025); ORCA (Mukherjee et al., 2023); Self-Instruct (Wang et al., 2022); Flan (Wei et al., 2022). (Approximately 6 methods) * **Retrieval (§4.2):** RAG (Lewis et al., 2020); REALM (Guu et al., 2020); kNN-LM (Retriever) (Izacard et al., 2021); Atlas (Jiang et al., 2022); ReAct (Yao et al., 2023); Dyna-PRM (Zhao et al., 2025b). (Approximately 6 methods) * **Generated Knowledge (§4.3):** G-RAG (Laskar et al., 2024); Know-Gen (Li et al., 2023); Graph-RAG (Wang et al., 2023); Self-Knowledge (Chen et al., 2023); Knowledge-Augmented LLM (Liu et al., 2023); Auto-Knowledge (Zou et al., 2023). (Approximately 6 methods) ### Key Observations * The diagram demonstrates a rapidly expanding field, with numerous methods being developed in each category. * The "Automated Supervision" branch under "Data Process" contains the largest number of methods, suggesting a strong focus on automating the process of creating effective prompts. * The "Generative PRMs" and "Continuous PRMs" categories within "Prompt Training" are also well-populated, indicating active research in these areas. * The citations indicate that much of this work is very recent (2023-2025), highlighting the dynamic nature of LLM prompting research. ### Interpretation This taxonomy provides a structured overview of the diverse landscape of prompting techniques for LLMs. The categorization by data process and training paradigm helps to understand the different approaches being taken to improve LLM performance. The diagram suggests a trend towards more automated and generative methods, likely driven by the desire to reduce the need for manual prompt engineering and to create more adaptable and robust prompting strategies. The sheer number of methods listed indicates a highly competitive research area, with ongoing efforts to explore new and innovative ways to elicit desired behavior from LLMs. The inclusion of section numbers suggests this diagram is part of a larger document or survey paper. The diagram is a valuable resource for researchers and practitioners seeking to navigate the complex world of LLM prompting. </details> mathematical reasoning, multimodal tasks, longhorizon web navigation, robustness testing, and cross-domain generalization. Overall, this diagram provides a visual roadmap of the survey: from how process-level data is built, to the modeling strategies and deployment of PRMs, and finally to the resources enabling evaluation and comparison. It helps readers navigate the field and locate specific methods or datasets within our proposed taxonomy.