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ReGRPO introduces a reflection-augmented policy optimization framework for tool-using vision-language agents, leveraging structured failure observations and joint optimization of reflection tokens and actions to improve recovery from tool failures, achieving state-of-the-art results on GTA and GAIA benchmarks.
This paper introduces Reflection-Augmented Scaling (RAS), a method that uses execution feedback from failed Cypher queries to iteratively refine query generation via in-context learning, reducing execution error rates by 41-50% across multiple datasets and models.