Cover Image

PRMBench: A Fine-grained and Challenging Benchmark for Process-Level Reward Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Song, Mingyang, Su, Zhaochen, Qu, Xiaoye, Zhou, Jiawei, Cheng, Yu
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Socratic-PRMBench: Benchmarking Process Reward Models with Systematic Reasoning Patterns
by: Li, Xiang, et al.
Published: (2025)

Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging
by: Lu, Zhenyi, et al.
Published: (2024)

LASP-2: Rethinking Sequence Parallelism for Linear Attention and Its Hybrid
by: Sun, Weigao, et al.
Published: (2025)

MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing
by: Li, Jiaqi, et al.
Published: (2024)

MedPRMBench: A Fine-grained Benchmark for Process Reward Models in Medical Reasoning
by: Wu, Lingyan, et al.
Published: (2026)

Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learning
by: Chen, Shuang, et al.
Published: (2025)

Fine-tuning Language Models with Generative Adversarial Reward Modelling
by: Yu, Zhang Ze, et al.
Published: (2023)

The Lessons of Developing Process Reward Models in Mathematical Reasoning
by: Zhang, Zhenru, et al.
Published: (2025)

SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation
by: Yang, Wenjie, et al.
Published: (2025)

Process Reward Models That Think
by: Khalifa, Muhammad, et al.
Published: (2025)

XSkill: Continual Learning from Experience and Skills in Multimodal Agents
by: Jiang, Guanyu, et al.
Published: (2026)

Timo: Towards Better Temporal Reasoning for Language Models
by: Su, Zhaochen, et al.
Published: (2024)

Linear-MoE: Linear Sequence Modeling Meets Mixture-of-Experts
by: Sun, Weigao, et al.
Published: (2025)

Process Reinforcement through Implicit Rewards
by: Cui, Ganqu, et al.
Published: (2025)

DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling
by: Wang, Minzheng, et al.
Published: (2024)

Fine-Tuning Language Models with Reward Learning on Policy
by: Lang, Hao, et al.
Published: (2024)

On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion
by: Fan, Chenghao, et al.
Published: (2024)

DiffETM: Diffusion Process Enhanced Embedded Topic Model
by: Shao, Wei, et al.
Published: (2025)

T-REG: Preference Optimization with Token-Level Reward Regularization
by: Zhou, Wenxuan, et al.
Published: (2024)

More Bang for the Buck: Process Reward Modeling with Entropy-Driven Uncertainty
by: Cao, Lang, et al.
Published: (2025)

ConflictBank: A Benchmark for Evaluating the Influence of Knowledge Conflicts in LLM
by: Su, Zhaochen, et al.
Published: (2024)

Unlocking Multimodal Mathematical Reasoning via Process Reward Model
by: Luo, Ruilin, et al.
Published: (2025)

RewardAnything: Generalizable Principle-Following Reward Models
by: Yu, Zhuohao, et al.
Published: (2025)

Aligning Large Language Models via Fine-grained Supervision
by: Xu, Dehong, et al.
Published: (2024)

Rewarding the Scientific Process: Process-Level Reward Modeling for Agentic Data Analysis
by: Qiu, Zhisong, et al.
Published: (2026)

Learning to Reason under Off-Policy Guidance
by: Yan, Jianhao, et al.
Published: (2025)

Towards Large Language Models that Benefit for All: Benchmarking Group Fairness in Reward Models
by: Song, Kefan, et al.
Published: (2025)

ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates
by: Yang, Ling, et al.
Published: (2025)

From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration
by: Song, Mingyang, et al.
Published: (2025)

Process Rewards with Learned Reliability
by: Li, Jinyuan, et al.
Published: (2026)

ExGRPO: Learning to Reason from Experience
by: Zhan, Runzhe, et al.
Published: (2025)

Efficient Process Reward Modeling via Contrastive Mutual Information
by: Lee, Nakyung, et al.
Published: (2026)

Mitigating Multilingual Hallucination in Large Vision-Language Models
by: Qu, Xiaoye, et al.
Published: (2024)

CoLD: Counterfactually-Guided Length Debiasing for Process Reward Models in Mathematical Reasoning
by: Zheng, Congmin, et al.
Published: (2025)

TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
by: Cai, Mu, et al.
Published: (2024)

RM-R1: Reward Modeling as Reasoning
by: Chen, Xiusi, et al.
Published: (2025)

TGDPO: Harnessing Token-Level Reward Guidance for Enhancing Direct Preference Optimization
by: Zhu, Mingkang, et al.
Published: (2025)

Reward Collapse in Aligning Large Language Models
by: Song, Ziang, et al.
Published: (2023)

LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning
by: Huang, Wei, et al.
Published: (2026)

RewardFlow: Topology-Aware Reward Propagation on State Graphs for Agentic RL with Large Language Models
by: Feng, Xiao, et al.
Published: (2026)