empo2-memory-augmented-llm-agent

Improve exploration in LLM-based agents through external memory-augmented RL with hybrid on/off-policy training. Agents generate exploration 'tips' (self-reflections) after trajectories, storing them in memory. During rollouts, policy samples between standard execution and memory-conditioned execution. Off-policy updates distill memory-guided behaviors into…

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