Update README.md

README.md

@@ -4,10 +4,14 @@ base_model:
 - Qwen/Qwen2.5-VL-7B-Instruct
 pipeline_tag: image-text-to-text
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+<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/blob/main/performance.jpg" width="90%"></img>
+  <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)