Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no NCCL
Researchers demonstrate a method to train AI models more efficiently by distributing work across multiple machines without specialized communication infrastructure.
AsyncGRPOTrainer is a system for training AI model adapters while running training and inference on separate machines using Hugging Face Jobs. The setup works by having the trainer save small adapter files to a shared storage bucket rather than syncing entire model weights across machines, which would be impractical without direct network connections between jobs. A proxy server routes requests to the inference replicas that already hold the necessary cached data and broadcasts adapter updates to all servers. The approach reduced training time from over three hours to 53 minutes for the same task by optimizing how data and model updates move between the separate training and inference components.
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Anthropic releases new Mythos and Fable models amid RSI concerns
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Also reported by The Verge, SiliconANGLE and MarkTechPost
Runway’s Praxis-1 world action model for robotics is now available
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Gemini 4 Carbon is now available
The latest version aims to deliver improved performance while reducing the computational resources and energy required for AI operations.
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Also reported by SiliconANGLE
Introducing AlphaProof Code: a breakthrough in verifying complex software at depth
AlphaProof Code agents wrote three million lines of proofs in Lean to verify Signal’s core encryption.