Join us for the first-ever SkyRL Meetup! It's been a year since we released the SkyRL library. SkyRL now has 2k+ GitHub stars and a growing user base in academia and industry, including researchers from Stanford, CMU, UC Berkeley, Microsoft AI, Datadog, and more. We're hosting a SkyRL Meetup on August 18th at the Anyscale office in SF. We have a special set of lightning talks: 🔗 Making frontier RL training accessible to everyone, from @trajectorylabs 🐶 Post-training an SRE agent with SkyRL, from @datadoghq ⚡️ Scaling RL with SkyRL on AMD GPUs, from @AMD 🧑🏻‍💻 Training knowledge work agents with SkyRL, from the SkyRL team (@charlie_ruan) We’ll also do a deep dive into the library and talk about the road ahead. More details here: luma.com/y4ahouh6

SkyRL now supports end-to-end vision-language post-training, from SFT to agentic RL, and adds vision model support to SkyRL’s Tinker interface! Existing multimodal cookbooks, e.g. VLM classification, work out of the box:

Can we train code agents to search relevant locations in a codebase only using a terminal? Introducing CodeScout: an effective RL recipe for code search 🚀 🏆 Outperforms 18x larger OSS LLMs 🔥 Comparable to proprietary LLMs 📈 SoTA on SWE-Bench Verified, Pro, & Lite 🧵 [1/N]

Introducing our new work K-Search: LLM Kernel Generation via Co-Evolving Intrinsic World Model — a new paradigm for automated GPU kernel generation, achieving SoTA results. 🔍 Big insight: Traditional methods treat LLMs as stochastic code generators inside heuristic loops — but this misses a key point: LLMs are powerful planners with rich domain priors. 🧠 Core idea: K-Search uses the LLM itself as a co-evolving world model — one that plans + updates beliefs + guides search decisions based on experience. 📌 This decouples high-level strategy (intent) from low-level code implementation, allowing the optimizer to pursue multi-step transformations even when intermediate implementations don’t immediately improve performance. 📈 Key results: 🔥 Our discovered kernels are ~2.10× average speedup vs state-of-the-art evolutionary search across 4 FlashInfer kernels on H100/B200. 🔥 Up to 14.3× gain on complex Mixture-of-Experts (MoE) kernels. 🔥 State-of-the-art performance on GPUMode TriMul (H100) task — beating both automated and human solutions. 🙏 Acknowledgements This work is developed in @BerkeleySky, w/ the amazing @ziming_mao, @profjoeyg, and @istoica05. We thank @DachengLi177, @MayankMish98, @randwalk0, @pgasawa, @fangz_zzu, and @tian_xia_ for helpful discussion and feedback. We also thank the generous compute support from @databricks, @awscloud, @anyscalecompute, @nvidia, @Google, @LambdaAPI, and @MayfieldFund. 👨‍💻 GitHub: github.com/caoshiyi/K-Search 📄 arXiv: arxiv.org/pdf/2602.19128v1

🔥Modifying 2 lines of code and get your agentic serving/rollout up to 3.9x faster losslessly! ⚡️Say hello to ThunderAgent, a fast, simple, and program-aware agentic Inference System. 🥇 We propose a program abstraction to schedule all GPU and CPU resources, the first principled approach for distributed agentic inference and rollout. 🌐 Blog: thunderagent.ai/ 💻 Code: github.com/ThunderAgent-org/… 📜 Paper: arxiv.org/pdf/2602.13692 #AI #ThunderAgent #LLMAgent #Mlsys 1/n

Releasing the official SkyRL + Harbor integration: a standardized way to train terminal-use agents with RL. From the creators of Terminal-Bench, Harbor is a widely adopted framework for evaluating terminal-use agents on any task expressible as a Dockerfile + instruction + test script. This integration extends it: the same tasks you evaluate on, you can now RL-train on. Blog: novasky-ai.notion.site/skyrl… 🧵

SkyRL now implements the Tinker API. Now, training scripts written for Tinker can run on your own GPUs with zero code changes using SkyRL's FSDP2, Megatron, and vLLM backends. Blog: novasky-ai.notion.site/skyrl… 🧵

(1/9) We built Endless Terminals: a fully autonomous pipeline that procedurally generates terminal tasks for RL training with no human annotation needed. Simple PPO + scaled environments give consistent improvements on downstream tasks like Terminal Bench 2.0!

Announcing OpenThoughts-Agent with an incredible team — a data-centric effort on TerminalBench-style tasks, built with SkyRL+Harbor 💻🤖 Co-leading the RL team over the past month has been a blast, and we’re just getting started! (1/n) 🧵

1/n 🚀 Introducing SkyRL-Agent, a framework for efficient RL agent training. ⚡ 1.55× faster async rollout dispatch 🛠 Lightweight tool + task integration 🔄 Backend-agnostic (SkyRL-train / VeRL / Tinker) 🏆 Used to train SA-SWE-32B, improving Qwen3-32B from 24.4% → 39.4% Pass@1 on SWE-Bench Verified with >2× lower cost GitHub: github.com/NovaSky-AI/SkyRL Paper link: arxiv.org/pdf/2511.16108 👇 more details

SkyRL now supports Megatron! Training massive MoE models demands more than just ZeRO-3/FSDP sharding. The Megatron backend for SkyRL unlocks high throughput training with: ✅ 5D parallelism (tensor + pipeline + context + expert + data) ✅ Efficient training for 30B+ MoEs

Training our advisors was too hard, so we tried to train black-box models like GPT-5 instead. Check out our work: Advisor Models, a training framework that adapts frontier models behind an API to your specific environment, users, or tasks using a smaller, advisor model (1/n)!

Introducing SkyRL tx 🧸, an open-source project to implement the Tinker API. The SkyRL team is excited about the Tinker API and the opportunities of using a single canonical interface that unifies training and inference. SkyRL tx lets you run a Tinker-like service locally today, but the project is early and we want to collaborate with you all! Blog: novasky-ai.notion.site/skyrl… Code: github.com/NovaSky-AI/SkyRL/…