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@@ -247,8 +247,8 @@ While generative reward models have shown strong performance in preference predi
\vspace{0.5em}
\item\textbf{Analytic Rubric.}
An analytic rubric evaluates each criterion separately. The criterion-level scores can then be averaged or weighted to obtain the final reward.
\item\textbf{Criterion-wise Scoring Rubric.}
A criterion-wise scoring rubric asks the reward model to assign a separate score to each evaluation criterion. These criterion-level scores are then averaged or weighted to obtain the final reward.
\vspace{0.5em}
\begin{tcolorbox}[frame empty]
...
...
@@ -357,14 +357,29 @@ While generative reward models have shown strong performance in preference predi
Two commonly used approaches for automatic rubric generation. We use checklist rubrics as an illustrative example.
}
\label{fig:rubric-based-reward-modeling}
\end{figure*}
It is worth noting that reasoning can also be incorporated into rubric-based evaluation. For example, in a checklist-style rubric, the reward model can be asked to reason about each criterion before producing the corresponding Pass/Fail judgment. Additionally, although we use pairwise evaluation, i.e., taking $(\mathbf{x}, \mathbf{y}_a, \mathbf{y}_b)$ as reward model input, as the running example, rubric-based reward modeling is equally applicable to pointwise evaluation, i.e., taking $(\mathbf{x}, \mathbf{y})$ as reward model input.
The quality of the rubric is crucial to the effectiveness of rubric-based reward modeling. As a result, recent work has devoted increasing attention to constructing high-quality rubrics for reward modeling. Similar to many other components in machine learning, rubric acquisition generally follows two approaches: manual design and automatic generation.
The quality of the rubric is crucial to the performance of rubric-based reward models. As a result, recent work has devoted increasing attention to constructing high-quality rubrics for reward modeling. Similar to many other components in machine learning, rubric acquisition generally follows two approaches: manual design and automatic generation.
For manual design, one straightforward approach is to recruit human experts to write rubrics based on task requirements and their domain knowledge. However, this way is often difficult to design a rubric that comprehensively covers the diverse cases that may arise across different inputs and tasks. This is because that evaluation criteria that are appropriate for one case may be insufficient for another. Additionally, manually designed rubrics inherit the prompt sensitivity of LLM-based evaluation: even when the underlying evaluation dimensions remain the same, small differences in wording can lead to noticeably different results.
For manual design, one straightforward approach is to recruit human experts to write rubrics based on their domain knowledge and task requirements. However, this way is often difficult to design a rubric that comprehensively covers the diverse cases that may arise across different inputs and tasks. This is because that evaluation criteria that are appropriate for one case may be insufficient for another. Additionally, manually designed rubrics inherit the prompt sensitivity of LLM-based evaluation: even when the underlying evaluation dimensions remain the same, small differences in wording can lead to noticeably different results.
One promising strategy is to generate rubrics dynamically based on the input. As shown in Figure~\ref{fig:rubric-based-reward-modeling}, existing approaches generally fall into two categories. The first uses an external LLM to generate task-specific rubrics from the input and evaluation requirements, which are then provided to the reward model for preference prediction. The second allows the generative reward model to generate its own rubrics for the current input and then evaluate candidate responses according to these self-generated criteria.
Both approaches formulate rubric generation as an explicit task. This naturally leads to another idea: can we optimize the rubric generation process itself to produce better rubrics? The answer is yes. Rubrics generated directly by LLMs may suffer from limited coverage, redundant criteria, or preference misalignment \citep{liu-etal:openrubrics,shen-etal:rethinking-rubric}. To improve rubric quality, we can explicitly refine the generated criteria. For example, OpenRubrics generates rubrics by contrasting preferred and rejected responses to identify more discriminative rules and principles \citep{liu-etal:openrubrics}. We can further learn the rubric generation process itself through RL training \citep{xu-etal:rubric-arm}. More recent studies go one step further by allowing rubrics to evolve with the policy, so that the evaluation criteria continue to capture new weaknesses as model improves \citep{rezaei-etal:online-rubrics,ding-etal:evorubrics,yu-etal:audio-rubrics}. Rubric optimization is becoming an active research direction, and interested readers can refer to the aforementioned works for further details.
