Summary of Offline Reinforcement Learning For Llm Multi-step Reasoning, by Huaijie Wang et al.
Offline Reinforcement Learning for LLM Multi-Step Reasoning
by Huaijie Wang, Shibo Hao, Hanze Dong, Shenao Zhang, Yilin Bao, Ziran Yang, Yi Wu
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an offline reinforcement learning method called OREO (Offline Reasoning Optimization) to improve the multi-step reasoning ability of large language models. The approach builds upon insights from maximum entropy reinforcement learning and jointly learns a policy model and value function by optimizing the soft Bellman Equation. This method reduces the need for paired preference data and enables better credit assignment in complex tasks. The paper demonstrates the effectiveness of OREO on various benchmarks, including mathematical reasoning tasks (GSM8K, MATH) and embodied agent control (ALFWorld). The learned value function can also be used to guide tree search during test time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making big language models better at solving complex problems. Right now, these models are great at answering simple questions, but they struggle with more challenging tasks that require thinking several steps ahead. To help them improve, the researchers created a new way of learning called OREO. This method allows the model to learn from its mistakes and make better decisions without needing lots of practice data. The results show that OREO is very good at solving complex problems, like math puzzles and controlling robots. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
