SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open
  Software Evolution
Authors: name: Yuxiang Wei, name: Olivier Duchenne, name: Jade Copet, name: Quentin Carbonneaux, name: Lingming Zhang, name: Daniel Fried, name: Gabriel Synnaeve, name: Rishabh Singh, name: Sida I. Wang

The recent DeepSeek-R1 release has demonstrated the immense potential of
reinforcement learning (RL) in enhancing the general reasoning capabilities of
large language models (LLMs). While DeepSeek-R1 and other follow-up work
primarily focus on applying RL to competitive coding and math problems, this
paper introduces SWE-RL, the first approach to scale RL-based LLM reasoning for
real-world software engineering. Leveraging a lightweight rule-based reward
(e.g., the similarity score between ground-truth and LLM-generated solutions),
SWE-RL enables LLMs to autonomously recover a developer's reasoning processes
and solutions by learning from extensive open-source software evolution data --
the record of a software's entire lifecycle, including its code snapshots, code
changes, and events such as issues and pull requests. Trained on top of Llama
3, our resulting reasoning model, Llama3-SWE-RL-70B, achieves a 41.0% solve
rate on SWE-bench Verified -- a human-verified collection of real-world GitHub
issues. To our knowledge, this is the best performance reported for
medium-sized (<100B) LLMs to date, even comparable to leading proprietary LLMs
like GPT-4o. Surprisingly, despite performing RL solely on software evolution
data, Llama3-SWE-RL has even emerged with generalized reasoning skills. For
example, it shows improved results on five out-of-domain tasks, namely,
function coding, library use, code reasoning, mathematics, and general language
understanding, whereas a supervised-finetuning baseline even leads to
performance degradation on average. Overall, SWE-RL opens up a new direction to
improve the reasoning capabilities of LLMs through reinforcement learning on
massive software engineering data.