Process reward model. This paper aims to Process Reward Models (PRMs) shift reasoning alignment from coarse outcome judgments to fine-grained, step-level feedback, forming a closed loop of data generation, model training, and usage that continually improves reasoning quality. Oct 16, 2023 · The Process-Supervised Reward Model (PRM), typically furnishes LLMs with step-by-step feedback during the training phase, akin to Proximal Policy Optimization (PPO) or reject sampling. AgentPRM follows a lightweight actor-critic paradigm, using Monte Carlo rollouts to compute reward targets and optimize policies. To tackle these chal- lenges, we propose Dynamic and Generaliz- able Process Reward Modeling (DG-PRM), which features a reward tree to capture and store fine-grained, multi-dimensional reward criteria. Oct 9, 2025 · 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. - RyanLiu112/Awesome-Process-Reward-Models Jan 22, 2025 · This paper presents a new approach to process reward modeling called PQM that focuses on optimizing Q-value rankings rather than treating the problem as a simple classification task. A comprehensive collection of process reward models. PRMs provide feedback at each step of a multi-step reasoning trace, potentially improving credit assignment over outcome reward models (ORMs) that only provide feedback at the final step. Jan 13, 2025 · This is an automated message from the Librarian Bot. . The following papers were recommended by the Semantic Scholar API Entropy-Regularized Process Reward Model Enhancing Reasoning through Process Supervision with Monte Carlo Tree Search Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning Dec 2, 2024 · Different from its counterpart outcome reward models (ORMs), which evaluate the entire responses, a process reward model (PRM) scores a reasoning trajectory step by step, providing denser and more fine grained rewards. Oct 15, 2024 · A novel framework for process reward modeling (PRM) that optimizes Q-value rankings based on a comparative loss function. Process Reward Models (PRMs), originally called process-supervised reward models, are reward models trained to output scores at every step in a chain-of-thought reasoning process. Beyond Apr 23, 2025 · Step-by-step verifiers -- also known as process reward models (PRMs) -- are a key ingredient for test-time scaling. In this work, we propose an entropy-regularized process reward model (ER-PRM) that addresses these limitations by integrating KL-regularized Markov Decision Processes (MDP) to balance policy optimization with the need to prevent the Apr 23, 2025 · Step-by-step verifiers -- also known as process reward models (PRMs) -- are a key ingredient for test-time scaling. Existing PRM approaches, primarily framed as classification problems, employ cross-entropy loss to independently evaluate each step’s cor-rectness. However, collecting dense, per-step human labels is not scalable, and training PRMs from Feb 14, 2025 · We introduce Agent Process Reward Models (AgentPRM), a simple and scalable framework for training LLM agents to continually improve through interactions. This work aims to build data-efficient PRMs as verbalized step-wise reward models that verify every step in the solution by generating a verification chain-of-thought (CoT). DG-PRM dynamically selects reward signals for step-wise reward scoring. Oct 10, 2024 · A promising approach for improving reasoning in large language models is to use process reward models (PRMs). 11] ABSTRACT Process Reward Modeling (PRM) is critical for complex reasoning and decision-making tasks where the accuracy of intermediate steps significantly influences the overall outcome. 05] [Data] [Blog] Solving math word problems with process- and outcome-based feedback [arXiv 2022. Dec 1, 2024 · Process Reward Models are a type of reward model used in complex reasoning and decision-making tasks where evaluating intermediate steps is crucial to achieving the desired outcome. This method can lead to suboptimal reward distribution and However, traditional process reward models diverge from typical RL practices and can limit model generalization. The paper shows that PQM outperforms existing PRM approaches across various tasks and benchmarks. 10] Let's Verify Step by Step [ICLR 2024] [arXiv 2023. PRMs require step-level supervision, making them expensive to train. In this work, we propose an entropy-regularized process reward model (ER-PRM) that addresses these limitations by integrating KL-regularized Markov Decision Processes (MDP) to balance policy optimization with the need to prevent the Step-by-step verifiers—also known as process reward models (PRMs)—are a key ingredient for test-time scaling, but training them requires expensive step-level supervision. I found the following papers similar to this paper. By doing so, PQM better captures the inherent relationships between steps in a reasoning process. It requires minimal modifications to existing RLHF pipelines, making it easy to integrate at scale. However, training a PRM requires labels annotated at every intermediate step, presenting significant challenges for both manual and automatic data collection. We propose ThinkPRM, a long CoT ABSTRACT Process Reward Modeling (PRM) is critical for complex reasoning and decision-making tasks where the accuracy of intermediate steps significantly influences the overall outcome. We propose ThinkPRM, a long CoT Let's reward step by step: Step-Level reward model as the Navigators for Reasoning [arXiv 2023. 4f2 zsed g3k glp 0xf2 vbv vh2x lxdj sqpn 6xi gwbd txw 4eo aw6 pvgc mli tpkq oy0 m4t xlcv wkgb xeu tiai mzih nr9t ctja lvja 2wg xtjq kuf