SWE-RL: Reinforcement Learning for LLMs on Software Evolution
from Neural intel Pod Hosted by Neuralintel.org
This paper introduces SWE-RL, a reinforcement learning (RL) method to improve large language models (LLMs) for software engineering tasks using software evolution data and rule-based rewards. The approach trains LLMs to autonomously learn from open-source software's lifecycle, including code snapshots, changes, and events. The resulting model, Llama3-SWE-RL-70B, achieves state-of-the-art performance among medium-sized models on SWE-bench Verified, a benchmark for solving real-world GitHub issues. Surprisingly, training with SWE-RL on software evolution data enhances the LLM's generalized reasoning skills, leading to improved performance on out-of-domain tasks like math and code generation. This highlights the potential of RL on software engineering data to improve LLM reasoning and the paper also introduces Agentless Mini, a framework that prioritizes straightforward component decomposition, parallelization, and scalability. Ultimately, this research paves the way for developing more powerful and reliable LLMs for software engineering.
Embed this episode
No transcript yet.