Update README.md

README.md

@@ -65,9 +65,9 @@ DeepSWE-Preview is trained on top of Qwen3-32B with thinking mode enabled. With
 Discover more about DeepSWE-Preview's development and capabilities in our [technical blog post](https://pretty-radio-b75.notion.site/DeepSWE-Training-a-Fully-Open-sourced-State-of-the-Art[%E2%80%A6]-by-Scaling-RL-22281902c1468193aabbe9a8c59bbe33).
 
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-  <img src="./figures/swebench.png" style="width: 100%;" />
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/654037be97949fd2304aab7f/FbSSr0XQRYfnoiczStZ-E.png" style="width: 100%;" />
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-    Figure 1: SWE-Bench-Verified Performance vs. Model Size for LLM Agents. Trained with only reinforcement learning (RL, no SFT), DeepSWE-Preview with test time scaling (TTS) solves 59% of problems, beating all open-source agents by a large margin. We note that DeepSWE-Preview's Pass@1 performance (42.1%, averaged over 16 runs) is one of best for open-weights coding agents.
+    Figure 1: SWE-Bench-Verified Performance vs. Model Size for LLM Agents. Trained with only reinforcement learning (RL, no SFT), DeepSWE-Preview with test time scaling (TTS) solves 59% of problems, beating all open-source agents by a large margin. We note that DeepSWE-Preview's Pass@1 performance (42.2%, averaged over 16 runs) is one of best for open-weights coding agents.
   </p>
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