SWE-RL by Meta — Reinforcement Learning for Software Engineering LLMs

In this video, we dive into a new Meta research paper: "SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution".

This paper introduces SWE-RL, a new reinforcement learning method for real-world software engineering. By training large language models (LLMs) directly on the evolution of real GitHub projects, SWE-RL can empower LLM to be better at software engineering.

We break down:

• How Meta curated 11 million pull requests from GitHub.

• SWE-RL training pipeline.

• SWE-RL state-of-the-art results on SWE-bench Verified for open-source models under 100B parameters.

🔗 Written Review: https://aipapersacademy.com/swe-rl/

🔗 Paper Link: https://arxiv.org/abs/2502.18449

🔗 Code: https://github.com/facebookresearch/swe-rl

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The video was edited using VideoScribe - https://tidd.ly/44TZEiX

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#airesearch #metaai #swe_rl #reinforcementlearning #llm

Chapters:

0:00 Introduction

1:15 GitHub PRs Curation

3:20 SWE-RL Training

5:42 Aha Moments

6:39 SWE-RL Results