Can an AI grader trained to check math steps also catch mistakes in finance, without starting over?
Can process reward models generalize across verification domains without retraining?
This explores whether a process reward model (a grader that scores each step of a reasoning chain) trained in one domain, like math, can be used in another, like finance, without being retrained.
This explores whether a process reward model (a grader that scores each step of a reasoning chain) trained in one domain can be used in another without retraining. The corpus has no clean test of this, meaning one grader run across several domains with no changes. What it does suggest is that transfer is unreliable when errors are about facts, and that recent work has mostly made retraining cheaper or skipped the trained grader instead of building a universal one.
The most direct evidence says no. Fin-PRM found that general-purpose process reward models miss factual and regulatory errors in financial reasoning, because a step can be logically tidy and still wrong about the rules. Beating them took a finance-specific model built on expert knowledge bases and supervised at both step and whole-trajectory level Can general process reward models catch factual errors in finance?. A grader trained on math has learned what a valid derivation looks like. It has not learned what a regulation says. The part that transfers is checking logical coherence, and the part that doesn't is domain knowledge.
What has changed is how much retraining costs. Generative process reward models such as GenPRM and ThinkPRM write out a chain of thought before giving a verdict. A 1.5B GenPRM beats GPT-4o, and ThinkPRM surpasses a fully trained discriminative verifier using about 1% of the PRM800K labels Can generative reasoning beat discriminative models with less training data?. StepWiser reaches the same conclusion independently: judges that reason about a step beat classifiers that just label it Can judges that reason about reasoning outperform classifier rewards?. Reward models that reason can also spend more test-time compute on harder cases Can reward models benefit from reasoning before scoring?. These papers measure data efficiency, not cross-domain transfer. It's a reasonable guess that a grader which states its reasoning adapts to a new domain with a few labels, but no note here tests that.
Other work avoids the question by not depending on a domain-trained grader. VeriFree uses the probability of a reference answer given the model's own reasoning as the reward. It matches verifier-based methods on MMLU-Pro, GPQA, and SuperGPQA, which span many domains Can reasoning improvement work without answer verification?. Model confidence can rank reasoning traces without any external verifier Can model confidence work as a reward signal for reasoning?. Checklist rewards break a vague goal into small, checkable sub-criteria, which avoids the superficial patterns holistic reward models latch onto Can breaking down instructions into checklists improve AI reward signals?. LongTraceRL mines process signals from what search agents read but never cite, with no separately trained grader Can search agent behavior yield reliable process rewards for reasoning?.
The question may be better split in two. A grader can probably transfer its sense of whether an argument hangs together. It can't transfer knowledge of what is true in a specialized field, and that has to be supplied through grounding or domain data. The gap in this library is a head-to-head study that runs the same process reward model across several domains and reports where it breaks.