Summary of Enhancing Llm Agents For Code Generation with Possibility and Pass-rate Prioritized Experience Replay, by Yuyang Chen et al.
Enhancing LLM Agents for Code Generation with Possibility and Pass-rate Prioritized Experience Replay
by Yuyang Chen, Kaiyan Zhao, Yiming Wang, Ming Yang, Jian Zhang, Xiaoguang Niu
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The proposed BTP pipeline for transformer-based Large Language Models (LLMs) tackles the efficiency challenge in code generation by incorporating Experience Replay (ER) in the fine-tuning phase. This novel approach, consisting of beam search sampling, testing, and prioritized experience replay, leverages failed programs collected by LLMs to improve performance. The P2Value metric comprehensively assesses possibility and pass rate, enabling the efficient reuse of redundant resources. Empirical evaluations demonstrate that BTP pipeline enhances LLMs’ code generation capabilities, surpassing existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformer-based Large Language Models (LLMs) are great at generating code, but they can be slow because they keep trying different versions until they find one that works. To speed things up, researchers came up with a new way to train LLMs using something called Experience Replay. This approach helps the models learn from their past mistakes and make better choices in the future. The new method, called BTP pipeline, has three stages: generating code, testing it, and then reusing successful attempts to improve performance. By reusing failed programs, the models can learn more quickly and accurately generate code. |
Keywords
» Artificial intelligence » Fine tuning » Transformer
