RLTF: Reinforcement Learning from Unit Test Feedback

Jiate Liu, yiqin zhu, Kaiwen Xiao, Qiang Fu, Xiao Han, Wei Yang, Deheng Ye

RLTF: Reinforcement Learning from Unit Test Feedback: 5 upvotes on Hugging Face Daily Papers, #9 of 14 papers on 2023-07-11. Day-by-day upvote history.

The goal of program synthesis, or code generation, is to generate executable code based on given descriptions. Recently, there has been an increasing number of studies employing reinforcement learning (RL) to improve the performance of large language models (LLMs) for code. However, these RL methods have only used offline frameworks, limiting their exploration of new sample spaces. Additionally, current approaches that utilize unit test signals are rather simple, not accounting for specific error locations within the code. To address these issues, we proposed RLTF, i.e., Reinforcement Learning from Unit Test Feedback, a novel online RL framework with unit test feedback of multi-granularity for refining code LLMs. Our approach generates data in real-time during training and simultaneously utilizes fine-grained feedback signals to guide the model towards producing higher-quality code. Extensive experiments show that RLTF achieves state-of-the-art performance on the APPS and the MBPP benchmarks. Our code can be found at: https://github.com/Zyq-scut/RLTF.

Paper page on Hugging Face · arXiv

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