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Stas Bekman

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https://stasosphere.com/machine-learning/

AI & ML interests

Toolmaker. Software creator, optimizer and harmonizer. Makes things work and fly at Snowflake AI Research Training LLM/RAG/Generative AI/Machine Learning/Scalability

Recent Activity

posted an update about 23 hours ago
In parallel we announce a new open source repo: https://github.com/Snowflake-AI-Research/Arctic-Platform This is the framework for very fast RL (and future other optimizations rolled into it) It currently has all the code you need to use or integrate Arctic RL into RL frameworks, with SkyRL and Verl available and more framework integrations coming. Please kindly spread the word! Thank you!
posted an update about 23 hours ago
After many months of intense work the Snowflake AI Research team is happy to present to you the new open source project: Arctic RL https://snowflake.com/en/blog/engineering/arctic-rl-open-source-backend/ - Arctic RL integrates with VeRL and SkyRL today; enable ZoRRo with one config flag, no code changes required - ZoRRo delivers up to 6x actor-update acceleration and a 3.5x end-to-end training speedup, reducing Arctic-Text2SQL-R2 training from ~5 days to ~36 hours on 32 H200 GPUs - Arctic-Text2SQL-R2 achieved higher accuracy scores (48.7) than Gemini 3.1 Pro (47.9) and Claude 4.7 (47.3) on Snowflake's evaluated enterprise SQL benchmark under the tested conditions - Two open source recipes ship with this release: a text-to-SQL recipe that improved BIRD dev accuracy from 59.92% to 70.35%, and a multi-hop QA recipe that improved average accuracy from 69.6% to 72.3%
posted an update 14 days ago
PSA for DeepSpeed users - a long outstanding precision-related critical bug has been identified and fixed in https://github.com/deepspeedai/DeepSpeed/pull/8066 and a new release has been made. The issue was about mixed precision mode downcasting buffers that had to be in fp32 - massively impacting correctness due to large static buffers - e.g. RoPE in Qwen3 models when using long sequence lengths 32K+. Hopefully this fix brings Deepspeed to a close parity with FSDP2 which has been an issue since a long time. You can still have the old behavior but you'd now need to manually configure it - by default the model's buffers will now remain in the original precision. Please install deepspeed==0.19.2 which will do the right thing. Thanks to Tunji Ruwase and Claude Opus 4.8 via Cursor for identifying and fixing the problem.
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