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app/src/index.html

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     <li><strong>Oracle: </strong>use the ground truth labels to estimate the pass@1 score per problem. Bin the distribution of pass@1 scores to determine the quintiles.</li>
     <li><strong>Model: </strong>use the distribution of average PRM scores per problem to determine the quintiles. The intuition here is that harder problems will have lower scores.</li>
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 <p id="15d1384e-bcac-80a3-af7c-f3497126ab1e" class="">Here’s the breakdown of the various methods according to the pass@1 scores and across four test-time compute budgets of \(N = [4,16,64, 256]\):</p><figure id="15b1384e-bcac-80ad-9cf3-cf5bcbd3f53b" class="image"><a href="https://huggingface.co/datasets/HuggingFaceH4/blogpost-images/resolve/main/levels-maj-bon-beam.png"><img style="width:707.9891357421875px" src="https://huggingface.co/datasets/HuggingFaceH4/blogpost-images/resolve/main/levels-maj-bon-beam.png"/></a></figure><p id="15d1384e-bcac-80c3-93b3-fa4c071ac807" class="">In this plot, each bar denotes a test-time compute budget, and within each bar we show the relative accuracy of each method. For example, in the group of four bars on difficulty level 2 we see that:</p>
 
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