Can verification accuracy scale without retraining the model?

Generation has three well-worn scaling axes — pre-training, post-training, test-time compute — and LLM-as-a-Verifier argues verification is a fourth that has not been scaled the same way. The claim is that determining whether a solution is correct is not a byproduct of a good generator but a separable capability with its own scaling dimensions: score granularity, repeated evaluation, and criteria decomposition. Each can be dialed up at inference, without additional training, to buy more verification accuracy.

This reframes a bottleneck. Since What is the actual reusable unit of reasoning data?, the verifier is the load-bearing component of reasoning training — yet current verifiers are weak: LM judges collapse into coarse discrete scores that tie, and learned reward models are stuck inside their training distribution and fail to generalize across domains. If verification is a scaling axis, then those failures are not fixed properties but under-scaled ones, and the way to a better verifier is to spend more inference compute on it rather than to train a bespoke reward model.

The three dimensions decompose the gains cleanly: granularity gives better positive/negative separation, repeated evaluation reduces variance, and criteria decomposition reduces complexity per judgment. This connects to the panel-of-judges result — Can smaller models in panels outperform a single large judge? is a special case of scaling repeated evaluation across evaluators — but LLM-as-a-Verifier generalizes it into a training-free framework that provides dense agentic feedback where discrete judges provide none.