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Donate arxiv logo > cs > arXiv:2412.16145 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Machine Learning arXiv:2412.16145 (cs) [Submitted on 20 Dec 2024] Title:Offline Reinforcement Learning for LLM Multi-Step Reasoning Authors:Huaijie Wang, Shibo Hao, Hanze Dong, Shenao Zhang, Yilin Bao, Ziran Yang, Yi Wu View a PDF of the paper titled Offline Reinforcement Learning for LLM Multi-Step Reasoning, by Huaijie Wang and 6 other authors View PDF Abstract:Improving the multi-step reasoning ability of large language models (LLMs) with offline reinforcement learning (RL) is essential for quickly adapting them to complex tasks. While Direct Preference Optimization (DPO) has shown promise in aligning LLMs with human preferences, it is less suitable for multi-step reasoning tasks because (1) DPO relies on paired preference data, which is not readily available for multi-step reasoning tasks, and (2) it treats all tokens uniformly, making it ineffective for credit assignment in multi-step reasoning tasks, which often come with sparse reward. In this work, we propose OREO (Offline Reasoning Optimization), an offline RL method for enhancing LLM multi-step reasoning. Building on insights from previous works of maximum entropy reinforcement learning, it jointly learns a policy model and value function by optimizing the soft Bellman Equation. We show in principle that it reduces the need to collect pairwise data and enables better credit assignment. Empirically, OREO surpasses existing offline learning methods on multi-step reasoning benchmarks, including mathematical reasoning tasks (GSM8K, MATH) and embodied agent control (ALFWorld). The approach can be extended to a multi-iteration framework when additional resources are available. Furthermore, the learned value function can be leveraged to guide the tree search for free, which can further boost performance during test time. Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2412.16145 [cs.LG] (or arXiv:2412.16145v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2412.16145 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Shibo Hao [view email] [v1] Fri, 20 Dec 2024 18:49:45 UTC (1,378 KB) Full-text links: Access Paper: View a PDF of the paper titled Offline Reinforcement Learning for LLM Multi-Step Reasoning, by Huaijie Wang and 6 other authors * View PDF * TeX Source * Other Formats view license Current browse context: cs.LG < prev | next > new | recent | 2024-12 Change to browse by: cs cs.AI cs.CL References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... 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