hansenhua/EAPO-ICML26

<p align="center"> <img src="https://github.com/HansenHua/EAPO-ICML26/raw/main/introduction.png" width="90%"></img> </p>

EAPO (Exploration-Aware Policy Optimization) is a reinforcement learning framework for training agentic large language models to perform adaptive exploration during test-time interaction. Unlike prior methods that apply exploration uniformly across all states, EAPO enables agents to selectively explore only when environmental uncertainty is high, improving long-horizon reasoning and decision making in interactive environments such as GUI control, web navigation, and embodied tasks.

<p align="center"> <img src="https://github.com/HansenHua/EAPO-ICML26/raw/main/performance.jpg" width="50%"></img> </p>

Paper: Learning to Explore: Scaling Agentic Reasoning via Exploration-Aware Policy Optimization (https://arxiv.org/abs/2605.08978)

Code: https://github.com/HansenHua/EAPO-ICML26