Reframe is a post-training framework that aligns the observation state, not just the output distribution, for agentic visual reasoning. The method includes GD-GRPO and OG-MOPD, which enhance the use of tool-returned observations and repair observations during rollouts.
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Reframe can be useful for improving agents' ability to effectively utilize tool-returned observations and recover behavior in complex visual tasks.
A2-MEAS, an embodied multi-agent framework using learned correction codes, enhances robot autonomy. The method achieves high performance on tasks like BEHAVIOR 2026 and RoboTwin 2.0, with significant improvements in task success and failure recovery.
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