Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards

• URL: http://arxiv.org/abs/2604.17957v1
• Date: Mon, 20 Apr 2026 08:39:13 GMT
• Title: Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards
• Abstract summary: This work introduces a novel and scalable approach to PRM dataset generation based on planning logical problems.<n>We generate a corpus of approximately one million reasoning steps across various PDDL domains and use it to train PRMs.<n> Experimental results show that augmenting widely-used PRM training datasets with PDDL-derived data yields substantial improvements in both mathematical and non-mathematical reasoning.
• Score: 38.453862515879514
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Process Reward Models (PRMs) have emerged as a powerful tool for providing step-level feedback when evaluating the reasoning of Large Language Models (LLMs), which frequently produce chains of thought (CoTs) containing errors even when the final answer is correct. However, existing PRM datasets remain expensive to construct, prone to annotation errors, and predominantly limited to the mathematical domain. This work introduces a novel and scalable approach to PRM dataset generation based on planning logical problems expressed in the Planning Domain Definition Language (PDDL). Using this method, we generate a corpus of approximately one million reasoning steps across various PDDL domains and use it to train PRMs. Experimental results show that augmenting widely-used PRM training datasets with PDDL-derived data yields substantial improvements in both mathematical and non-mathematical reasoning, as demonstrated across multiple benchmarks. These findings indicate that planning problems constitute a scalable and effective resource for generating robust, precise, and fine-grained training data for PRMs, going beyond the classical mathematical sources that dominate this field.

Related papers

• TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular Reasoning [77.01182934427095]
  TaTToo is a novel table-grounded PRM framework that integrates tool-based verification to provide precise reward supervision.<n>We train TaTToo with a dual-stage paradigm: cold-start supervised fine-tuning to capture tool-use reasoning patterns, followed by reinforcement learning to align our model with table-based verification.
  arXiv  Detail & Related papers  (2025-10-07T17:59:41Z)
• Uncertainty-Based Methods for Automated Process Reward Data Construction and Output Aggregation in Mathematical Reasoning [10.227089771963943]
  We propose an uncertainty-driven framework for automated process reward data construction.<n>We introduce two generic uncertainty-aware output aggregation methods: Hybrid Majority Reward Vote and weighted Reward Frequency Vote.<n>Experiments on ProcessBench, MATH, and GSMPlus show the effectiveness and efficiency of the proposed PRM data construction framework.
  arXiv  Detail & Related papers  (2025-08-03T14:14:13Z)
• ReasonFlux-PRM: Trajectory-Aware PRMs for Long Chain-of-Thought Reasoning in LLMs [75.72672339168092]
  We introduce ReasonFlux-PRM, a novel trajectory-aware PRM to evaluate trajectory-response type of reasoning traces.<n>ReasonFlux-PRM incorporates both step-level and trajectory-level supervision, enabling fine-grained reward assignment aligned with structured chain-of-thought data.<n>Our derived ReasonFlux-PRM-7B yields consistent performance improvements, achieving average gains of 12.1% in supervised fine-tuning, 4.5% in reinforcement learning, and 6.3% in test-time scaling.
  arXiv  Detail & Related papers  (2025-06-23T17:59:02Z)
• DreamPRM: Domain-Reweighted Process Reward Model for Multimodal Reasoning [32.943971698086735]
  We introduce DreamPRM, a domain-reweighted training framework for multimodal PRMs.<n>In the lower-level optimization, DreamPRM performs fine-tuning on multiple datasets with domain weights.<n>In the upper-level optimization, the PRM is evaluated on a separate meta-learning dataset.
  arXiv  Detail & Related papers  (2025-05-26T17:20:17Z)
• From Mathematical Reasoning to Code: Generalization of Process Reward Models in Test-Time Scaling [32.72867198629561]
  We investigate the interplay between pre-training and reward model training FLOPs to assess their influence on PRM efficiency and accuracy.<n>Our findings indicate that PRMs trained on mathematical datasets exhibit performance comparable to those tailored for code generation.
  arXiv  Detail & Related papers  (2025-05-24T12:44:15Z)
• Generalizable Process Reward Models via Formally Verified Training Data [13.781401358802462]
  FoVer is an approach to synthesize PRM training data with accurate step-level error labels automatically annotated by formal verification tools.<n>Experiments show that PRMs trained with FoVer exhibit cross-task generalization, enabling a single PRM to effectively perform verification across diverse reasoning tasks.
  arXiv  Detail & Related papers  (2025-05-21T19:23:45Z)
• 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)
• Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models [46.75272311751018]
  We propose a novel reward model approach called the Hierarchical Reward Model.<n>It evaluates both individual and consecutive reasoning steps at both fine-grained and coarse-grained levels.<n>It excels at assessing multi-step reasoning coherence, especially when flawed steps are later corrected through self-reflection.
  arXiv  Detail & Related papers  (2025-03-16T15:18:40Z)
• 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.