Alibaba Model Beats Google, NVIDIA

- RynnBrain was developed by Alibaba's DAMO Academy and is led by Zhao Deli, the head of the Embodied Intelligence Lab. The model is built upon Alibaba's Qwen3-VL vision-language model and was optimized using a self-developed architecture called RynnScale, which reportedly doubled the training speed. - The model's core technical innovations are "spatiotemporal memory" and "physical-space reasoning." Spatiotemporal memory allows the robot to recall the historical state of objects, enabling it to resume interrupted tasks, while physical-space reasoning uses a combination of textual and spatial cues to ground its understanding in the real world. - Alibaba has released several open-source versions of RynnBrain, including models with 2 billion and 8 billion parameters, as well as a 30 billion-parameter Mixture-of-Experts (MoE) variant. The 30B MoE model is notably efficient, requiring only 3 billion active parameters during inference to outperform models with 72 billion parameters. - In addition to the foundational models, three specialized, post-trained versions were also released: RynnBrain-Plan for robot task planning, RynnBrain-Nav for vision-language navigation, and RynnBrain-CoP for chain-of-point reasoning. - To address a lack of detailed evaluation methods in the industry, DAMO Academy also introduced a new open-source benchmark called RynnBrain-Bench. This benchmark is designed to assess fine-grained spatiotemporal embodied AI tasks across four dimensions: object cognition, spatial cognition, grounding, and pointing. - A demonstration video titled "RynnBrain's Housework Diary" showcases the model's capabilities in a domestic setting. In the video, a robot performs tasks such as arranging tableware according to specific instructions, selecting a specific number of oranges from a bowl of mixed fruit, and retrieving items from a refrigerator.