Flex-Judge: Think Once, Judge Anywhere
Authors: name: Jongwoo Ko, name: Sungnyun Kim, name: Sungwoo Cho, name: Se-Young Yun

Human-generated reward signals are critical for aligning generative models
with human preferences, guiding both training and inference-time evaluations.
While large language models (LLMs) employed as proxy evaluators, i.e.,
LLM-as-a-Judge, significantly reduce the costs associated with manual
annotations, they typically require extensive modality-specific training data
and fail to generalize well across diverse multimodal tasks. In this paper, we
propose Flex-Judge, a reasoning-guided multimodal judge model that leverages
minimal textual reasoning data to robustly generalize across multiple
modalities and evaluation formats. Our core intuition is that structured
textual reasoning explanations inherently encode generalizable decision-making
patterns, enabling an effective transfer to multimodal judgments, e.g., with
images or videos. Empirical results demonstrate that Flex-Judge, despite being
trained on significantly fewer text data, achieves competitive or superior
performance compared to state-of-the-art commercial APIs and extensively
trained multimodal evaluators. Notably, Flex-Judge presents broad impact in
modalities like molecule, where comprehensive evaluation benchmarks are scarce,
underscoring its practical value in resource-constrained domains. Our framework
highlights reasoning-based text supervision as a powerful, cost-effective
alternative to traditional annotation-intensive approaches, substantially
advancing scalable multimodal model-as-a-judge.