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Arxiv
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Offline Reinforcement Learning for LLM Multi-Step Reasoning
- Offline reinforcement learning (RL) is proposed to improve the multi-step reasoning ability of large language models (LLMs).
- The method called OREO (Offline Reasoning Optimization) jointly learns a policy model and value function by optimizing the soft Bellman Equation.
- OREO reduces the need to collect pairwise data and enables better credit assignment in multi-step reasoning tasks.
- Empirically, OREO surpasses existing offline learning methods on multi-step reasoning benchmarks.
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