Tag
APEx is a hierarchical experience utilization framework that organizes agent history into memories and skills, using a closed-loop architecture to improve deep research question answering, achieving state-of-the-art performance surpassing GPT-5.4.
EDGE introduces a framework for guided exploration in agentic reinforcement learning by distilling experiences into policies, improving performance on tasks like ALFWorld and WebShop.
Proposes Experience Distillation, a method that internalizes in-context learning gains from agent interaction histories into model weights without requiring additional environment interaction, achieving significant sample efficiency improvements on software engineering and text-adventure tasks.