• [2025/05/06] 🎉 We released SkyRL-v0: our open RL training pipeline for multi-turn tool use LLMs, optimized for long-horizon, real-environment tasks like SWE-Bench!

• 📜 SkyRL-v0 Blog Post

This repository contains training code for the SkyRL-v0 release. Our implementation is a fork of VeRL.
The repo is currently utilizing the SGLang async rollout feature introduced to VeRL in this draft PR, based on this commit. We will refactor the code soon so that the codebase can easily keep up with VeRL main branch.

The first step is to clone our repository:

git clone --recurse-submodules https://github.com/NovaSky-AI/SkyRL

For detailed installation instructions, please refer to INSTALL.md

For reproducing our results for SkyRL-Agent-14B-v0, SkyRL-Agent-8B-v0, and SkyRL-Agent-7B-v0 you can refer to examples/sky.

We report the evaluation result on SWE-Bench-Verified below:

This work is done at Berkeley Sky Computing Lab, with the amazing compute support from Anyscale, Databricks, NVIDIA, Lambda Labs, and AMD.

We would also like to thank Ying Sheng, Chenyang Zhao from SGLang team for supporting SGLang async rollout integration, and Kaichao You, Simon Mo from vLLM team for supporting vLLM performance optimization.

The code in this repository is mostly described in the post below. Please consider citing this work if you find the repository helpful.

@misc{cao2025skyrl,
  title     = {SkyRL-v0: Train Real-World Long-Horizon Agents via Reinforcement Learning},
  author    = {Shiyi Cao and Sumanth Hegde and Dacheng Li and Tyler Griggs and Shu Liu and Eric Tang and Jiayi Pan and Xingyao Wang and Akshay Malik and Graham Neubig and Kourosh Hakhamaneshi and Richard Liaw and Philipp Moritz and Matei Zaharia and Joseph E. Gonzalez and Ion Stoica},
  year      = {2025},
}