Exploring the Potential of Offline RL for Reasoning in LLMs: A Preliminary Study

• URL: http://arxiv.org/abs/2505.02142v1
• Date: Sun, 04 May 2025 15:09:49 GMT
• Title: Exploring the Potential of Offline RL for Reasoning in LLMs: A Preliminary Study
• Authors: Xiaoyu Tian, Sitong Zhao, Haotian Wang, Shuaiting Chen, Yiping Peng, Yunjie Ji, Han Zhao, Xiangang Li,
• Abstract summary: Long-context reasoning by large language models (LLMs) incurs substantial computational costs and complexity.<n>We investigate the effectiveness of Offline RL methods, specifically Direct Preference Optimization (DPO) and its length-desensitized variant LD-DPO.<n>Experiments demonstrate that these simpler Offline RL methods substantially improve model performance, achieving an average enhancement of 3.3%.
• Score: 16.441081996257576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite significant advances in long-context reasoning by large language models (LLMs), primarily through Online Reinforcement Learning (RL) methods, these approaches incur substantial computational costs and complexity. In contrast, simpler and more economical Offline RL methods remain underexplored. To address this gap, we investigate the effectiveness of Offline RL methods, specifically Direct Preference Optimization (DPO) and its length-desensitized variant LD-DPO, in enhancing the reasoning capabilities of LLMs. Extensive experiments across multiple reasoning benchmarks demonstrate that these simpler Offline RL methods substantially improve model performance, achieving an average enhancement of 3.3\%, with a particularly notable increase of 10.1\% on the challenging Arena-Hard benchmark. Furthermore, we analyze DPO's sensitivity to output length, emphasizing that increasing reasoning length should align with semantic richness, as indiscriminate lengthening may adversely affect model performance. We provide comprehensive descriptions of our data processing and training methodologies, offering empirical evidence and practical insights for developing more cost-effective Offline RL approaches.

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