RLTF: Reinforcement Learning from Unit Test Feedback
Authors: name: Jiate Liu, name: Yiqin Zhu, name: Kaiwen Xiao, name: Qiang Fu, name: Xiao Han, name: Wei Yang, name: Deheng Ye

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.