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About this paper
Stephen Chung, Wenyu Du, Jie Fu
This paper presents a multi-attempt reinforcement learning framework that enables language models to learn from their failures through iterative attempts and feedback. The approach improves problem-solving performance on mathematical benchmarks, achieving accuracy gains of approximately 7 percentage points with additional attempts, and also enhances performance in single-attempt evaluations.
Key insights
- Allowing models to learn from failures through iterative attempts significantly enhances their reasoning capabilities.
- The multi-attempt framework fosters the emergence of self-correction, more comprehensive reasoning, and alternative solution exploration.
- Skills developed in a multi-attempt training environment transfer and improve performance even in standard single-attempt evaluation scenarios.
Summary by alphaXiv.
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