RLTF: Reinforcement Learning from Unit Test Feedback

Jiate Liu, Yiqin Zhu, Kaiwen Xiao, QIANG FU, Xiao Han, Yang Wei, Deheng Ye

Abstract:

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, some of the current representative RL methods have only used offline frameworks, limiting the exploration of new sample spaces.
Additionally, the utilization of unit test signals is limited, 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 is available at: \url{https://github.com/Zyq-scut/RLTF}.

Submission Length: Regular submission (no more than 12 pages of main content)

Code: https://github.com/Zyq-scut/RLTF

Assigned Action Editor: ~Swarat_Chaudhuri1

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Submission Number: 1353