NovaSky @NovaSkyAI

🏹5 Days of Trajectory. Day 3 - An Open Source Training Stack for Continual Learning Building the platform for continual learning requires both partnering with pioneering AI companies, as we showed on Day 2 with Harvey, and working toward frontier research, which we are highlighting today. Continual learning means models that improve hourly from real production use. But with the size of frontier models, this becomes quite difficult. A Qwen-397b would need to spin up and tear down repeatedly across six GPU nodes, and that's valuable time gone. Our contribution is Continual LoRA (C-LoRA): many lightweight adapters running at once on one shared base model. Our insight centers on where the parallelism lives: instead of splitting one giant job across nodes, we load-balance many small jobs over a single base. The result: 2.81x experiment throughput over single-tenant training, with no regression on rewards. We built this together, with @anyscalecompute, @NovaSkyAI, and generous support from @GoogleCloud and @GoogleStartups. We've open-sourced on SkyRL as one of the first multi-LoRA, RL training platforms, so that every team can get to continual learning faster. We’re very excited to see what you build, please reach out!

We've shipped two major upgrades for RL✨! 1. Native weight syncing APIs: Standardizes weight transfer, provides optimized implementations for NCCL and CUDA IPC out of the box, and also lets frameworks easily bring their own. 2. Improved pause/resume for Async RL: Careful coordination between DP ranks so that engines don’t deadlock. Validated at scale in P/D, wide-EP setups! In collaboration with @anyscalecompute, @NovaSkyAI, and @RedHat. More and more RL frameworks are using vLLM as the default for inference, details in the blog 👇 vllm.ai/blog/2026-05-2…

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Amazing work! More and more RL frameworks are using vLLM as default. @vllm_project along with @anyscalecompute and @NovaSkyAI revamped weight syncing and improved wide-ep deployment for rollout!

Sumanth Hegde @sumanthrh

a week ago

Excited to share some of our work on improving vLLM for RL! A number of RL frameworks, including SkyRL, use vLLM for inference, and we’ve noticed some common problems: 1. Weight syncing between training and inference is implemented in an ad-hoc fashion and duplicated across

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it's been a really great experience working w/ @j316chuck and the @trajectorylabs team on building out their post-training stack for continual learning on top of SkyRL really excited to continue collaborating and seeing how the team can push the frontier for continual learning!

Ronak Malde @rronak_

a week ago

Today, @MichaelElabd, @QuantumArjun, and I are excited to announce Trajectory. We are a research lab and product company building the platform for Continual Learning. Our platform unlocks the signal already sitting in product usage, so companies can continuously post-train

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Awesome to see Trajectory labs launch out of stealth! It's been great collaborating with them in building out multi-LoRA for SkyRL!

jenn @jennzhaii

a week ago

So excited to share that I’ve joined @trajectorylabs! We’re pushing the frontier of RL research to build the platform for continual learning - systems that learn and evolve alongside your products in real time. We believe in a world where everyone has the power to own their own

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Today, @MichaelElabd, @QuantumArjun, and I are excited to announce Trajectory. We are a research lab and product company building the platform for Continual Learning. Our platform unlocks the signal already sitting in product usage, so companies can continuously post-train large-scale agentic models that outperform the frontier. @trajectorylabs We’ve raised $15M from @Conviction, @BessemerVP, @radicalvcfund, @jeffdean, @drfeifei and more. We’re partnering with some of the best AI-native companies: @ClayRunHQ @Harvey, @DecagonAI, @mercor_ai, @RogoAI to power their agentic systems, some of which we are already in production with. We’ve brought together a world class research team from DeepMind, OpenAI, Apple, Meta Superintelligence, Amazon AGI, Scale AI, and an elite product team from Stripe and Figma. AI will never again start on day one. Every correction, every retry, every edit will make products smarter. This is Continual Learning.

Super cool work built with SkyRL! Have also thought about how it's so wasteful not to train on the observation tokens. Can't wait to upstream it:)

Dimitris Papailiopoulos @DimitrisPapail

3 weeks ago

x.com/i/article/2056…

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A 4B RLM trained with SkyRL that matches Sonnet 4.6, very cool work!

alphaXiv @askalphaxiv

3 weeks ago

Reinforcing Recursive Language Models Can a 4B model learn to recursively call itself to answer hard long-context questions? We RL fine-tuned a small model to behave as a native RLM. On evidence selection across scientific papers, our 4B RLM matches Sonnet 4.6 in quality

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Check out VLM training in SkyRL! SFT / multi-turn RL, LoRA / full-finetuning, tinker-compatible as well!

Nithin Chalapathi @nithinch10

a month ago

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:

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🚀 Excited to share the training & inference results for UCCL-EP: a portable, high-performance expert-parallel communication library across heterogeneous GPU + NIC hardware. 💻 Code: github.com/uccl-project/u… 📝 Blog: uccl-project.github.io/posts/uccl-ep-… 📈 Highlights: • Up to 45% faster Megatron-LM training vs RCCL on 128 AMD GPUs • Up to 40% faster SGLang inference vs NCCL on 32 H200 GPUs • Up to 25% lower vLLM TPOT vs NCCL • Up to 2.3x better EP dispatch/combine on AWS EFA 🔁 Fully portable across heterogeneous GPU/NIC hardware and a drop-in replacement for DeepEP Amazing team: Chon Lam Lao, @yangzhouy, Yihan Zhang, Chihan Cui, Zhongjie Chen, Zhiying Xu, @KaichaoYou, Zhen Huang, Zhenyu Gu, Costin Raiciu, Scott Shenker, @istoica05

Great work from the @OpenHandsDev community and CMU! Open source SOTA on code localization via RL. Happy to see the beautiful reward curves trained with SkyRL!

Aditya Soni @Aditya_Soni_8

3 months ago

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]

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Excited to see SkyRL sharing their work on inference and vLLM in RL at the LLMs on Ray office hours this Thursday. If you’re exploring using vLLM in RL workflows, this will be a great session to join. See you there 👇

Seiji Eicher @seiji_________

3 months ago

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

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We’ve been consistently surprised lately by how capable frontier models are at handling complex kernel implementation and system optimization. Check out this work as a step toward automating AI infrastructure building!

Shiyi Cao @shiyi_c98

3 months ago

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

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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…

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Excited to see SkyRL being used by systems research to study how agentic RL workload can be optimized!! github.com/ThunderAgent-o…

Hao Kang @GT_HaoKang

4 months ago

🔥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

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Train your terminal-use agents with SkyRL+Harbor!

Charlie Ruan @charlie_ruan

4 months ago

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

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A 30-minute talk on SkyRL’s most recent updates:)

Huaizheng Zhang @zhzHNN

4 months ago

LLM RL Training with SkyRL. One of the best RL sharing you must watch. youtu.be/MrJNri6ysYQ

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A very cool project built with SkyRL!!

Kanishk Gandhi @gandhikanishk

4 months ago

(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!

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