NovaSky
136 posts
@NovaSkyAI
Building SkyRL at @BerkeleySky Join the Slack community: https://t.co/mSO97T61vR
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-tinker 🧵
Hi all, extending the invite to the LLMs on Ray office hours next Thursday, 3/5 9:30-10:30AM PT! We will be hosting @erictang000 and @sumanthrh from the @NovaSkyAI SkyRL project to present on inference/vLLM in RL and take questions from the group. After, there will be time for any other questions folks have on distributed inference w/ Ray. Hope you can make it! Sign up for the invite here: forms.gle/QESMQ8ojRJsCZV…
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-Sea… 📄 arXiv: arxiv.org/pdf/2602.19128…
🔥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-o… 📜 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-harbor 🧵
LLM RL Training with SkyRL. One of the best RL sharing you must watch. youtu.be/MrJNri6ysYQ
(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) 🧵
How can we make a better TerminalBench agent? Today, we are announcing the OpenThoughts-Agent project. OpenThoughts-Agent v1 is the first TerminalBench agent trained on fully open curated SFT and RL environments. OpenThinker-Agent-v1 is the strongest model of its size on TerminalBench, and sets a new bar on our newly released OpenThoughts-TB-Dev benchmark. (1/n)
