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

2025-05-04

AI Summary

Online reinforcement learning (RL) for long-context reasoning in large language models (LLMs) incurs prohibitive computational cost and complexity. Method: This paper systematically explores offline RL, focusing on direct preference optimization (DPO) and introducing length-debiasing DPO (LD-DPO)—a novel variant that mitigates output-length bias by prioritizing semantic depth over step-wise redundancy. We construct a length-aware preference dataset and reward modeling framework tailored to long-chain reasoning. Contribution/Results: We provide the first empirical validation of DPO-based methods for long-context reasoning. Evaluations across multiple benchmarks—including Arena-Hard—demonstrate average reasoning performance gains of 3.3%, with a 10.1% improvement on Arena-Hard. Crucially, our approach significantly reduces training overhead while ensuring reproducibility, establishing an efficient, scalable offline RL paradigm for LLM reasoning optimization.

Technology Category

• Search and Optimization: Learning to Search
• Knowledge Representation and Reasoning: Preferences
• Natural Language Processing: (Large) Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Investigating Offline RL for cost-effective LLM reasoning enhancement
• Evaluating DPO and LD-DPO on reasoning benchmarks
• Analyzing output length sensitivity in RL-based reasoning

Innovation

• Uses Offline RL for LLM reasoning enhancement
• Applies DPO and LD-DPO to improve performance
• Analyzes output length sensitivity in reasoning