LegalRewardBench: Benchmarking Reward Models for Legal Preference Evaluation

Abstract:Reward models are central to preference alignment for legal tasks, yet existing reward model benchmarks are still primarily centered on general-domain preferences. In this work, we introduce LegalRewardBench, the first human-verified benchmark for evaluating reward models in the legal domain. The benchmark organizes legal preference evaluation into five capability levels: knowledge, understanding, analysis, decision, and writing, and preference examples are constructed through rubric-guided automatic judging combined with human verification. We evaluate three families of judges, covering both general and fine-tuned models, and generative and discriminative architectures. Experimental results show that strong general-purpose generative models are currently the most robust legal reward models, while existing fine-tuned reward models transfer less reliably to legal preference evaluation. We also conduct detailed comparisons of input organizations and training strategies.

Computation and Language (cs.CL)

arXiv:2610.18299 [cs.CL]

(or arXiv:2610.18299v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.18299 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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