We are happy to announce SkyRL tx 0.0.3! SkyRL tx is an open source library that implements a backend for the Tinker API and allows people to set up their own Tinker-like service running on their own hardware. This release has full MoE support, better checkpointing and the first…
novasky-ai.notion.site
SkyRL tx v0.0.3 Release
Philipp Moritz, Tyler Griggs, and the SkyRL Team
i don't understand the asynchronous rl claim for higher throughput. you can colocate training and generation on the same set of gpus and the switching bottleneck is minimal. this still achieves high throughput while avoiding off policy training.
practical, modern GRPO tweaks as described in Meta's Code World Models paper
thank god kl is useless 🙏 fucking hate having to deal with the ref model
I am making agents that fix performance bottlenecks in code. Here, it made search in @WerWolv ImHex 2x faster! End-to-end, producing a ready-to-compile updated project with no changes in functionality
great read
Meet SFR-DeepResearch (SFR-DR) 🤖: our RL-trained autonomous agents that can reason, search, and code their way through deep research tasks. 🚀SFR-DR-20B achieves 28.7% on Humanity's Last Exam (text-only) using only web search 🔍, browsing 🌐, and Python interpreter 🐍,…
rl frameworks fail or succeed based on how photons hit silicon on your laptop. the same script that worked 12 days ago with pinged dependencies and versions now fails
Sometimes I open my stripe and realize my saas autopilot side hustle casually made $150 yesterday 🦍
i saw another tweet about someone beating a benchmark with rl after training directly on it and open sourcing the project. what's literally the point? are startups just showing off or have we forgotten basic train/val/test splits ever since rl went mainstream?
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