From Passive Observer to Active Critic: Reinforcement Learning Elicits Process Reasoning for Robotic Manipulation

Accurate process supervision remains a critical challenge forlong-horizon robotic manipulation. A primary bottleneck is that currentvideo MLLMs, trained primarily under a Supervised Fine-Tuning (SFT)paradigm, function as passive “Observers” that recognize ongoing eventsrather than evaluating the current state relative to the final task goal.In this paper, we introduce PRIMO R1 (Process Reasoning InducedMOnitoring), a 7B framework that transforms video MLLMs into ac-tive “Critics”. We leverage outcome-based Reinforcement Learning to in-centivize explicit Chain-of-Thought generation for progress estimation.Furthermore, our architecture constructs a structured temporal inputby explicitly anchoring the video sequence between initial and currentstate images. Supported by the proposed PRIMO Dataset and Bench-mark, extensive experiments across diverse in-domain environments andout-of-domain real-world humanoid scenarios demonstrate that PRIMOR1 achieves state-of-the-art performance. Quantitatively, our 7B modelachieves a 50% reduction in the mean absolute error of specialized rea-soning baselines, demonstrating significant relative accuracy improve-ments over 72B-scale general MLLMs. Furthermore, PRIMO R1 exhibitsstrong zero-shot generalization on difficult failure detection tasks. Weestablish state-of-the-art performance on the RoboFail benchmark with67.0% accuracy, surpassing closed-source models like OpenAI o1 6.0%.The project website is: 10-oasis-01.github.io/primo-r1-website.