The Bidirectional Process Reward Model

• URL: http://arxiv.org/abs/2508.01682v1
• Date: Sun, 03 Aug 2025 09:23:49 GMT
• Title: The Bidirectional Process Reward Model
• Authors: Lingyin Zhang, Jun Gao, Xiaoxue Ren, Ziqiang Cao,
• Abstract summary: We propose a novel bidirectional evaluation paradigm, named Bidirectional Process Reward Model (BiPRM)<n>BiPRM seamlessly incorporates a parallel right-to-left (R2L) evaluation stream alongside the conventional L2R flow, enabling later reasoning steps to help assess earlier ones in real time.<n>We conduct extensive experiments on two mathematical reasoning benchmarks using samples generated by three different policy models.
• Score: 9.082060895625958
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Process Reward Models (PRMs) have emerged as a promising approach to enhance the reasoning quality of Large Language Models (LLMs) by assigning fine-grained scores to intermediate reasoning steps within a solution trajectory. However, existing PRMs predominantly adopt a unidirectional left-to-right (L2R) evaluation paradigm, which limits their ability to leverage global context, making it challenging to verify the consistency of earlier steps based on later ones. In light of these challenges, we propose a novel bidirectional evaluation paradigm, named Bidirectional Process Reward Model (BiPRM). BiPRM seamlessly incorporates a parallel right-to-left (R2L) evaluation stream alongside the conventional L2R flow, enabling later reasoning steps to help assess earlier ones in real time. Notably, the built-in R2L evaluation is implemented solely through prompt modifications that reverse the original reasoning trajectory, without any additional parameters or inference latency introduced. This ensures BiPRM remains both efficient and broadly compatible with existing PRM studies. We conduct extensive experiments on two mathematical reasoning benchmarks using samples generated by three different policy models. Our method, BiPRM, is evaluated across three backbones and three distinct PRM objectives. Across all settings, BiPRM consistently outperforms unidirectional baselines, achieving up to a 31.9% improvement in stepwise reward evaluation. Generally, our results highlight BiPRM's effectiveness, robustness, and general applicability, offering a promising new direction for process-based reward modeling.

Related papers

• SPARE: Single-Pass Annotation with Reference-Guided Evaluation for Automatic Process Supervision and Reward Modelling [70.01883340129204]
  Single-Pass.<n>with Reference-Guided Evaluation (SPARE)<n>Novel structured framework that enables single-pass, per-step annotation by aligning each solution step to one or multiple steps in a reference solution, accompanied by explicit reasoning for evaluation.<n>SPARE achieves competitive performance on challenging mathematical datasets while offering 2.6 times greater efficiency, requiring only 38% of the runtime.
  arXiv  Detail & Related papers  (2025-06-18T14:37:59Z)
• Beyond the First Error: Process Reward Models for Reflective Mathematical Reasoning [49.21525229904197]
  We propose a novel data annotation method for PRMs specifically designed to score the long CoT reasoning process.<n>We introduce the concepts of Error Propagation and Error Cessation, enhancing PRMs' ability to identify both effective self-correction behaviors and reasoning based on erroneous steps.<n>Our PRM achieves superior performance across various metrics, including search guidance, BoN, and F1 scores.
  arXiv  Detail & Related papers  (2025-05-20T14:12:05Z)
• R-PRM: Reasoning-Driven Process Reward Modeling [53.06844294668382]
  Process Reward Models (PRMs) have emerged as a promising solution by evaluating each reasoning step.<n>Existing PRMs typically output evaluation scores directly, limiting both learning efficiency and evaluation accuracy.<n>We propose Reasoning-Driven Process Reward Modeling (R-PRM)<n>R-PRM generates seed data from limited annotations, effectively bootstrapping our model's reasoning capabilities.
  arXiv  Detail & Related papers  (2025-03-27T09:23:08Z)
• Better Process Supervision with Bi-directional Rewarding Signals [41.929678717412266]
  We introduce BiRM, a novel process supervision model that evaluates correctness of previous steps and also models the probability of future success.<n>We conduct extensive experiments on mathematical reasoning tasks and demonstrate that BiRM provides more precise evaluations of LLM reasoning steps.<n>In search-based strategies, BiRM provides more comprehensive guidance and outperforms ORM by 5.0% and PRM by 3.8% respectively on MATH-500.
  arXiv  Detail & Related papers  (2025-03-06T17:03:17Z)
• ReARTeR: Retrieval-Augmented Reasoning with Trustworthy Process Rewarding [25.329712997545794]
  We propose Retrieval-Augmented Reasoning through Trustworthy Process Rewarding (ReARTeR)<n>ReARTeR enhances RAG systems' reasoning capabilities through post-training and test-time scaling.<n> Experimental results on multi-step reasoning benchmarks demonstrate significant improvements.
  arXiv  Detail & Related papers  (2025-01-14T05:56:26Z)
• The Lessons of Developing Process Reward Models in Mathematical Reasoning [62.165534879284735]
  Process Reward Models (PRMs) aim to identify and mitigate intermediate errors in the reasoning processes.<n>We develop a consensus filtering mechanism that effectively integrates Monte Carlo (MC) estimation with Large Language Models (LLMs)<n>We release a new state-of-the-art PRM that outperforms existing open-source alternatives.
  arXiv  Detail & Related papers  (2025-01-13T13:10:16Z)
• Entropy-Regularized Process Reward Model [30.279394036823092]
  Large language models (LLMs) have shown promise in performing complex multi-step reasoning, yet they continue to struggle with mathematical reasoning.<n>We propose an entropy-regularized process reward model (ER-PRM) that integrates KL-regularized Markov Decision Processes (MDP)<n>Our empirical experiments on the MATH and GSM8K benchmarks demonstrate that ER-PRM consistently outperforms existing process reward models.
  arXiv  Detail & Related papers  (2024-12-15T01:09:23Z)
• Learning to Refine with Fine-Grained Natural Language Feedback [81.70313509881315]
  We propose looking at refinement with feedback as a composition of three distinct LLM competencies.<n>A key property of the proposed Detect, Critique, Refine ("DCR") method is that the step 2 critique model can give fine-grained feedback about errors.<n>We show that models of different capabilities benefit from refining with DCR on the task of improving factual consistency of document grounded summaries.
  arXiv  Detail & Related papers  (2024-07-02T16:15:01Z)
• Let's reward step by step: Step-Level reward model as the Navigators for Reasoning [64.27898739929734]
  Process-Supervised Reward Model (PRM) furnishes LLMs with step-by-step feedback during the training phase. We propose a greedy search algorithm that employs the step-level feedback from PRM to optimize the reasoning pathways explored by LLMs. To explore the versatility of our approach, we develop a novel method to automatically generate step-level reward dataset for coding tasks and observed similar improved performance in the code generation tasks.
  arXiv  Detail & Related papers  (2023-10-16T05:21:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.