Can general process reward models catch factual errors in finance?

Process Reward Models supervise intermediate reasoning steps, but existing PRMs are trained mostly on general or STEM data and fall short where reasoning is structured, symbolic, and sensitive to factual and regulatory correctness — finance being the exemplar. Fin-PRM is a domain-specialized, trajectory-aware PRM that integrates step-level and trajectory-level reward supervision and, critically, includes verifiable reward components grounded in an expert-derived knowledge base. It supports the three standard PRM uses — selecting trajectories for distillation SFT, dense rewards for RL, and reward-informed Best-of-N at test time — and outperforms general-purpose PRMs on CFLUE and FinQA.

The keeper is the thesis the experiments validate: for high-stakes domains, effective process supervision requires a reward model that is not just logically coherent but deeply specialized and factually grounded. A general PRM can certify that a financial reasoning step follows from the previous one while the step asserts a regulatorily false premise; Fin-PRM's knowledge-aware components move it from assessing plausibility to penalizing factual hallucination. The dependence on a resource-intensive expert-derived dataset is the acknowledged cost.

This refines the vault's PRM cluster with a domain axis. Where Can generative reasoning beat discriminative models with less training data? improves PRM efficiency and Can self-supervised process rewards replace human annotation? improves PRM scalability, Fin-PRM argues that in truth-non-negotiable domains neither substitutes for knowledge grounding — the reward must verify facts, not only logic.