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RL agents can learn far more efficiently by incorporating group-level natural language feedback, achieving 2.2x sample efficiency gains in sparse-reward environments.
By integrating hierarchical memory and agentic RL, EventMemAgent enables MLLMs to actively perceive and reason about unbounded video streams, outperforming passive processing methods.
