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About this paper
Yanlin Wang, Yanli Wang, Daya Guo, Jiachi Chen, Ruikai Zhang, Yuchi Ma, Zibin Zheng
RLCoder presents a reinforcement learning framework for repository-level code completion that trains a retriever using perplexity-based rewards, thereby eliminating the need for labeled data. The system achieved up to 12.2% Exact Match improvement over state-of-the-art baselines on Python code completion benchmarks while enhancing context understanding through natural candidate construction and a stop signal mechanism.
Key insights
- Reinforcement learning with generative model feedback (perplexity) provides an effective method for training retrievers in code completion, eliminating the need for extensive human-labeled data.
- Careful design of code candidate construction, such as the 'Split-Aggregate' strategy, significantly improves the semantic coherence and utility of retrieved context.
- Intelligently deciding when to use external context and filtering irrelevant retrieved snippets (via a 'stop signal') is crucial for preventing hallucinations and enhancing code generation accuracy.
Summary by alphaXiv.
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