Open-Sourced Embodied Video Model LingBot-Video
HeyNayeem · x · 2026-07-12
Robbyant has open-sourced LingBot-Video, emphasizing that it is designed specifically as a foundation model for embodied intelligence (a "robot brain"), rather than for general-purpose video generation.
Key highlights:
- Evaluations for action-conditioned generation/rollout show it generalizes to unseen data rather than just memorizing training trajectories.
- The post-training phase utilizes 6 types of reward signals: aesthetic score, visual quality, image-text alignment, dynamics, motion consistency, human motion consistency, and a specialized physical feasibility signal.
- Data-wise, beyond internet video, it incorporates over 70,000 hours of embodied data spanning robotic manipulation, navigation, and egocentric content, trained via a 5-stage curriculum from 192p → 1080p.
- Architecture is Sparse MoE: 30B total parameters with only 3B activated per step. The author notes that on 1M token sequences, throughput reaches roughly 3x that of an equivalently sized Dense 30B model.
- On evaluation, it scores an average of 0.620 on RBench, leading both open and closed-source baselines in the table. However, the closed-source Wan 2.6 still outperforms in spatial, single-arm, and dual-arm tasks, though it doesn't dominate universally.
Related event: Embodied Video Model LingBot-Video Goes Open Source(4 posts)→
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