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RoboReason: Improving robotics-relevant VLM reasoning with scalable data generation

RoboReason proposes constructing spatial and temporal ordering tasks from real robot stereo camera and wrist camera images to evaluate the robotics-relevant reasoning capabilities of vision-language models; experiments show that several advanced VLMs perform poorly on the ordering tasks. The authors accordingly propose a scalable autonomous training data generation pipeline, and supervised fine-tuning and reinforcement learning on this data both significantly improve model performance on spatiotemporal ordering and downstream robotics and visual reasoning tasks. The work was accepted to the EMNLP 2026 main conference.

Part of the event: RoboReason proposes spatiotemporal ordering benchmark and data generation method for robot VLMs

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