@motubrain is streaming Motubrain 2.0 for two weeks: pre/post-training, agent execution, RSI, failures and fixes.
Physical AI, built in public. Exactly the kind of action-model workload vLLM-Omni is built to serve. Follow the run.
Motubrain 2.0 streams two weeks of Physical AI training publicly
AIMotubrain is live-streaming the full training pipeline for its Motubrain 2.0 Physical AI model over two weeks, covering pre-training, post-training, agent execution, and RSI. The stream will show failures and fixes along the way, and the model is slated for open-source release. vLLM-Omni is positioned as the serving stack for this action-model workload.
@vllm_project
Starting today, Motubrain 2.0 is live-streaming the full Physical AI training pipeline, continuously for half a month. Follow the training, testing, and capability development of this soon-to-be-open-source model. What you’ll see: · How do training stages connect and capabilities evolve · How an agent plans tasks and adjusts its actions based on feedback · Whether the model can transfer existing capabilities to tasks unseen during training · Whether AI can achieve self-improvement through RSI exploration Research rarely follows a straight path. Join us as we share progress, work through challenges, and discuss what we learn. We’d love to hear your observations, questions, and ideas along the way.
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Original title: @motubrain is streaming Motubrain 2.0 for two weeks: pre/post-training, agent execution, RSI, failures and fixes.