Summary of Process Supervision-guided Policy Optimization For Code Generation, by Ning Dai et al.
Process Supervision-Guided Policy Optimization for Code Generation
by Ning Dai, Zheng Wu, Renjie Zheng, Ziyun Wei, Wenlei Shi, Xing Jin, Guanlin Liu, Chen Dun, Liang Huang, Lin Yan
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A reinforcement learning (RL) approach with unit test feedback has improved large language models’ (LLMs) code generation, but relies on sparse rewards provided only after complete code evaluation, limiting learning efficiency and incremental improvements. To address this, a Process Reward Model (PRM) is proposed to deliver dense, line-level feedback on code correctness during generation, mimicking human code refinement and providing immediate guidance. Strategies for training PRMs and integrating them into the RL framework are explored, finding that using PRMs both as dense rewards and for value function initialization significantly boosts performance. The effectiveness of PRMs in enhancing RL-driven code generation is also demonstrated, especially for long-horizon scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning with unit test feedback helps large language models generate better code, but it’s slow because it only gets feedback after the whole thing is done. This makes it hard to improve little by little. To fix this, a new way of giving feedback is proposed that tells the model which specific lines of code are good or bad as they’re being written. This helps the model learn faster and make better choices about what to write next. The paper shows how this new approach can be used in combination with reinforcement learning to generate even better code, especially for longer programs. |
Keywords
» Artificial intelligence » Reinforcement learning
