NovaSky-AI (Berkeley Sky Computing Lab, with Anyscale) · RL training library

SkyRL

SkyRL is a modular, full-stack reinforcement learning library for LLMs from NovaSky-AI, developed at the Berkeley Sky Computing Lab in collaboration with Anyscale. It has four components. `skyrl` is the unified library for RL on your own hardware and supports the Tinker API; it combines the earlier `skyrl-train` training framework with `skyrl-tx`, a cross-platform Tinker API backend that unifies training and inference. `skyrl-agent` is an agent layer for training long-horizon, multi-turn tool-use agents, and `skyrl-gym` is a set of tool-use environments (maths, coding, search and SQL) built on the Gymnasium API. Recent README news includes day-0 support for Reflection AI's Beam, a 501B-A23B MoE model, with LoRA, full fine-tuning and MXFP8 RL recipes on a separate branch, and an end-to-end long-horizon knowledge-work recipe on the APEX-Agents dataset with Mercor. The code is Apache-2.0 licensed with 2,482 stars on GitHub; the latest release is skyrl-v0.4.0 (1 October 2026), following v0.3.0 (17 July 2026) and v0.2.0 (23 April 2026).