https://arxiv.org/abs/2504.02495 Skip to main content Cornell University We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > cs > arXiv:2504.02495 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Computation and Language arXiv:2504.02495 (cs) [Submitted on 3 Apr 2025] Title:Inference-Time Scaling for Generalist Reward Modeling Authors:Zijun Liu, Peiyi Wang, Runxin Xu, Shirong Ma, Chong Ruan, Peng Li, Yang Liu, Yu Wu View a PDF of the paper titled Inference-Time Scaling for Generalist Reward Modeling, by Zijun Liu and 7 other authors View PDF Abstract:Reinforcement learning (RL) has been widely adopted in post-training for large language models (LLMs) at scale. Recently, the incentivization of reasoning capabilities in LLMs from RL indicates that $\textit{proper learning methods could enable effective inference-time scalability}$. A key challenge of RL is to obtain accurate reward signals for LLMs in various domains beyond verifiable questions or artificial rules. In this work, we investigate how to improve reward modeling (RM) with more inference compute for general queries, i.e. the $\textbf {inference-time scalability of generalist RM}$, and further, how to improve the effectiveness of performance-compute scaling with proper learning methods. For the RM approach, we adopt pointwise generative reward modeling (GRM) to enable flexibility for different input types and potential for inference-time scaling. For the learning method, we propose Self-Principled Critique Tuning (SPCT) to foster scalable reward generation behaviors in GRMs through online RL, to generate principles adaptively and critiques accurately, resulting in $\textbf{DeepSeek-GRM}$ models. Furthermore, for effective inference-time scaling, we use parallel sampling to expand compute usage, and introduce a meta RM to guide voting process for better scaling performance. Empirically, we show that SPCT significantly improves the quality and scalability of GRMs, outperforming existing methods and models in various RM benchmarks without severe biases, and could achieve better performance compared to training-time scaling. DeepSeek-GRM still meets challenges in some tasks, which we believe can be addressed by future efforts in generalist reward systems. The models will be released and open-sourced. Comments: Preprint, under review. 42 pages Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2504.02495 [cs.CL] (or arXiv:2504.02495v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2504.02495 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Zijun Liu [view email] [v1] Thu, 3 Apr 2025 11:19:49 UTC (3,815 KB) Full-text links: Access Paper: View a PDF of the paper titled Inference-Time Scaling for Generalist Reward Modeling, by Zijun Liu and 7 other authors * View PDF * TeX Source * Other Formats license icon view license Current browse context: cs.CL < prev | next > new | recent | 2025-04 Change to browse by: cs cs.AI cs.LG References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... 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