Shengshu Technology’s world model team has started a public livestream of the full Motubrain 2.0 training process on Oct. 21, Beijing time, with the broadcast scheduled to run for half a month. According to Techub News, the stream is designed to show the public how an embodied model is trained and how the research progresses over time.
The livestream is set to cover four parts of the workflow: pre-training, post-training, Agent task execution, and exploration of recursive self-improvement, or RSI. The team said the goal is to present the full path from training and testing to capability iteration, rather than only showing final results.
Motubrain is Shengshu Technology’s embodied model series, built to provide robots with a general-purpose “brain.” The team said the open broadcast is meant to open up the “black box” of embodied model training, giving researchers and developers a closer look at concrete issues that arise during training and how those issues are addressed. It also aims to share experience with technical peers and discuss topics including large-scale training and cross-task generalization.
Techub News reported that Shengshu Technology’s world model team officially began a public livestream of the full Motubrain 2.0 training process on Oct. 21, Beijing time. The livestream is scheduled to continue for half a month and will show the public the training process and research progress of an embodied model.
The broadcast will cover four stages: pre-training, post-training, Agent task execution, and exploration of recursive self-improvement, or RSI. The team said the stream is intended to show the full process of how the model moves from training and testing to capability iteration.
The Motubrain series is Shengshu Technology’s embodied model line, aimed at building a general-purpose “brain” for robots.
The team said the move is meant to open the “black box” of embodied model training, giving researchers and developers a chance to see specific problems that emerge during training and the process of making improvements. It also plans to share experience with technical peers and discuss key topics such as large-scale training and cross-task generalization.
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