ReAgent-V is an agentic video understanding framework whose critic agent generates real-time rewards to refine answers and filter training data, yielding gains of up to 6.9%, 2.1%, and 9.8% across three applications.
Supervised learning
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ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding
ReAgent-V is an agentic video understanding framework whose critic agent generates real-time rewards to refine answers and filter training data, yielding gains of up to 6.9%, 2.1%, and 9.8% across three applications.