We introduce RynnUnify, a 16B unified embodied model that combines strong embodied understanding with long-horizon visual planning through interleaved video--language reasoning. With capabilities spanning spatial reasoning, physical prediction, and grounding, RynnUnify connects an understanding of the physical world with visual anticipation of how it evolves across successive subtasks. Building on these capabilities, we further develop an agentic extension through reinforcement learning to selectively use video predictions for reasoning about unobserved states and interaction outcomes. We also develop a world-action extension of the full RynnUnify model, translating its understanding and prediction capabilities into robot control. To evaluate these capabilities, we introduce Rynn-VP and Rynn-GEU for long-horizon visual planning and anticipatory reasoning. Experiments show that RynnUnify achieves the highest average embodied understanding score among the evaluated unified multimodal models and improves the Rynn-VP overall score by 9.75 points over Cosmos 3 Nano. Its agentic extension improves the Rynn-GEU average by 6.0 points over direct reasoning, while the world-action extension achieves 51.5% success on 20 selected composite RoboCasa365 tasks and 46.7% average success across three real-robot tasks. Together, these results establish RynnUnify as a shared foundation for embodied understanding, visual planning, and robot control, taking a step toward a unified embodied brain.
Figure 2: Overview of the RynnUnify architecture. (a) An autoregressive reasoner and a rectified-flow video generator interact through joint attention. (b) Memory-conditioned video generation supports I2V, FL2V, and autoregressive video continuation. Scene-memory frames provide persistent visual references, while a fixed temporal prefix carries recent motion and interaction context across consecutive clips.
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Subtask / Mode
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Duration (h)
Table 1: Overview of the data used for reasoner and generator fine-tuning in RynnUnify, covering general understanding, visual planning, and embodied perception and reasoning. We report sample counts for understanding and clip counts and approximate durations for video generation.