Update index.html

Author: chi558

index.html

@@ -142,7 +142,7 @@ <h2 class="title is-3">Overview</h2>
                     We introduce <b>AceCoder</b>, the first work to propose a fully automated pipeline for synthesizing large-scale reliable tests used for the reward model training and reinforcement learning in the coding scenario. To do this, we curated the dataset <b>AceCode-89K</b>, where we start from a seed code dataset and prompt powerful LLMs to "imagine" proper test cases for the coding question and filter the noisy ones. We sample inferences from existing coder models and compute their pass rate as the reliable and verifiable rewards for both training the reward model and conducting the reinforcement learning for coder LLM.
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-                    <img src="static/images/ac_overview.png" alt="algebraic reasoning" width="100%"/>
+                    <img src="static/images/crl_teaser.jpg" alt="algebraic reasoning" width="100%"/>
                     <p> Overall Workflow of our model: We start from the seed code dataset to create well-formatted questions and corresponding test cases. Then we adopt strong models like GPT-4o-mini to filter the noisy test cases. Finally, we adopt these test cases to harvest positive and negative program pairs for reward model training and RL.</p>
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