Summary of Reflectioncoder: Learning From Reflection Sequence For Enhanced One-off Code Generation, by Houxing Ren et al.
ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation
by Houxing Ren, Mingjie Zhan, Zhongyuan Wu, Aojun Zhou, Junting Pan, Hongsheng Li
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 presents ReflectionCoder, a novel approach that leverages compiler feedback to improve one-off code generation performance. The method integrates reflection sequences and proposes self-distillation and dynamically masked distillation techniques to effectively utilize these sequences. The authors demonstrate the effectiveness of their approach by fine-tuning models on three benchmarks (HumanEval, MBPP, and MultiPl-E), achieving state-of-the-art performance. Notably, ReflectionCoder-DeepSeek-Coder-33B outperforms GPT-3.5-Turbo and Claude-3-opus on HumanEval (+) and MBPP (+). The authors suggest that this approach can benefit other domains requiring long reasoning paths. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computers generate code more accurately. It uses information from the computer’s compiler to help it write better code. The authors tested their method on three different tasks and found that it works really well, even better than some other popular methods. They think this approach could be useful in other areas where computers need to reason deeply about complex problems. |
Keywords
» Artificial intelligence » Claude » Distillation » Fine tuning » Gpt
