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section4/section4.tex

@@ -214,6 +214,8 @@ Although incorporating CoT rationales into generative reward models improves pre
 
 
 
 
 \subsubsection{Rubric-based Reward Models}
 \subsubsection{Rubric-based Reward Models}
+For open-ended tasks, output quality is often multi-dimensional and highly dependent on the specific instruction. As a result, directly making a holistic judgment requires the reward model to implicitly determine both \emph{what aspects should be evaluated} and \emph{how well the output satisfies them}. This is challenging. In practice, such holistic evaluation can lead to biased, inconsistent, and less transparent judgments. For example, when evaluating an output to a writing task, the model may need to simultaneously consider factuality, relevance, clarity, and style. A single holistic reward score (or preference label) may overemphasize one aspect, such as fluency, while overlooking others. To provide comprehensive preference judgments, we can employ rubric-based reward modeling. This method is ....
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