Clarify a_i vs s_i

app/src/index.html

@@ -214,7 +214,7 @@
 
         $$ a_\mathrm{weighted} = \arg\max_{a} \sum_{i=1}^{N} \mathbb{I}(a_i = a) \cdot \mathrm{RM}(p, s_i) \,,$$
 
-        where \(\mathrm{RM}(p, s_i)\) is the reward model score of the \(i\)-th solution solution \(s_i\) to problem \(p\).</li>
+        where \(\mathrm{RM}(p, s_i)\) is the reward model score of the \(i\)-th solution \(s_i\) to problem \(p\). Here, a solution \(s_i\) refers to the concatenation of all steps from the model, while \(a_i\) refers to the final answer, typically extracted from <code>\boxed{answer}.</code></li>
     </ul>
 
     <p id="15d1384e-bcac-8012-8282-c0ed1215a611" class="">Typically, one usually uses an outcome reward model (ORM) to get a single, solution-level score. But to allow for fair comparison with the other search strategies discussed later, we will use the same PRM to score the solutions from Best-of-N. As illustrated below, PRMs produce a <em>cumulative</em> <em>sequence of step-level scores</em> per solution, so we need to perform a reduction over the steps to obtain a single solution-level score: </p><figure id="15d1384e-bcac-80d6-815f-c7d87fe313a6" class="image"><a href="https://huggingface.co/datasets/HuggingFaceH4/blogpost-images/resolve/main/prm-reductions.png"><img style="width:700px" src="https://huggingface.co/datasets/HuggingFaceH4/blogpost-images/resolve/main/prm-reductions.png"/></a></figure><p id="15d1384e-bcac-80e7-8d1a-e0aab286f9f4" class="">In the literature, the most common reductions are the following:</p>