Xiaomi Publicly Streams MiMo-V2.6 Reinforcement Learning Training

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Xiaomi announced a project by live-streaming the reinforcement learning training of its MiMo-V2.6 model. Led by Luo Fuli, the team has been focused on scaling reinforcement learning for nearly six months. V2.6 is still under training, with open-sourcing expected soon. The process uses 1,568 prompts per step, generating 16 rollouts each, totaling 2 billion tokens. Tasks including code, general, visual, cybersecurity, and chat agents are trained simultaneously. On-chain observers can track metrics such as task composition and training duration in real time. UC Berkeley’s Yichuan Wang noted that over 40,000 sandboxes are active simultaneously.
ME AI News, Dongcha Beating AI Bulletin: Xiaomi is live-streaming the reinforcement learning training of its next-generation MiMo-V2.6. Lead researcher Luo Fuli stated that the team has been silent for nearly six months, researching how far reinforcement learning can be scaled. V2.6 is still under training, and specific technical details will be open-sourced in the coming weeks. This time, the scale was set extremely high from the start: each step uses 1,568 prompts, generating 16 rollouts each, totaling approximately 2 billion tokens, all running asynchronously. Agent tasks including code, general reasoning, vision, cybersecurity, and chat are mixed within the same training session and utilize multiple harnesses simultaneously. The public can view real-time internal metrics such as task composition, context length, and training duration per step. Yichuan Wang, a PhD student at UC Berkeley, observed from the dashboard that each step involves about 1,500 tasks, with code tasks accounting for roughly two-thirds, and over 40,000 active sandboxes running concurrently. (Source: BlockBeats)
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