The Large Model Systems Organization develops large models and systems that are open, accessible, and scalable.
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Training SWE agents with Miles on MI355X: taming long-tail trajectories and complex sandbox coordination
We trained Qwen3-30B-A3B-Instruct with fully asynchronous agentic RL on AMD Instinct MI355X using Miles, SGLang, Megatron-LM, and Harbor, nearly quadrupling its pass@1 score on a 100-task stratified subset of SWE-bench Verified, from 7.3% to 27.5%.
SGLang security update: improving request isolation
On September 9, 2026, Cloudflare notified us of cross-request KV cache exposure in its GLM-5.3 deployment on Workers AI. We worked with Cloudflare to address the request-rejection path involved and strengthen related cache-lifetime handling in disaggregated serving.
miniTE Technical Report: Stable and Efficient Reinforcement Learning with Native NVFP4
Native NVFP4 reinforcement learning must remain stable under quantization and execution error while delivering practical efficiency gains. We investigate late-training instability by examining quantization formats, activation scaling, KV-cache precision, and framework differences that affect policy probabilities.
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