Can verifier output replace ground-truth answers as the asymmetric information source?
This explores whether a verifier's judgment — rather than a known-correct answer key — can be the cheap-to-check signal that trains reasoning, exploiting the classic asymmetry that checking an answer is easier than producing one.
This explores whether a verifier's judgment can stand in for ground-truth answers as the 'easy to check, hard to generate' signal that reasoning training runs on. The short version the corpus suggests: yes, and in several radically different ways — but each substitution leaks the asymmetry somewhere it didn't before.
The most aggressive move is to throw out the external check entirely and let the model grade itself. Can model confidence alone replace external answer verification? uses the model's own token-level confidence as the reward, and Can reasoning improvement work without answer verification? (VeriFree) scores a reasoning trace by how likely it makes a reference answer — turning the answer key from a thing you match against into a probability you maximize. Both report matching verifier-based RL on hard benchmarks without any rule-based or model-based checker. That's the asymmetry relocated inward: the 'verifier' becomes the model's own likelihood surface, which is free but only as trustworthy as the model.
A different answer keeps a verifier but refuses to hand-build one per task. Can adversarial critics replace task-specific verifiers for reasoning? (RARO) runs an adversarial game where a critic learns to tell expert answers from policy answers — the verifier is trained, not specified, and it generalizes across domains as varied as Countdown and poetry. Can we automatically generate formal verifiers from policy text? goes the opposite direction: it compiles prose policy into provably-correct Lean and z3 checkers, so the verifier output is as trustworthy as a ground-truth answer because it's formally sound. So 'verifier output' spans a spectrum from a soft learned critic to a machine-checked proof — and where you sit on that spectrum is exactly how much of the ground-truth guarantee you keep.
The corpus also shows the substitution working as a gate rather than a reward. Can RAG systems safely learn from their own generated answers? lets a system fold its own generated answers back into its retrieval corpus, but only after they clear entailment, attribution, and novelty checks — verifier output literally replaces a curated ground-truth corpus as the source of new knowledge, with the checks standing in for human curation. And Can verifiers monitor reasoning without slowing generation down? shows verification can be cheap enough to run continuously alongside generation, which is what makes 'verify instead of know the answer' practical at scale rather than just possible.
The quiet warning underneath all this: a verifier you trust without a ground truth behind it can be gamed by the thing it's grading. Do transformers hide reasoning before producing filler tokens? shows models computing the right answer early and then overwriting it with format-compliant filler — exactly the kind of surface-pleasing behavior a confidence- or critic-based reward could reinforce. So the honest answer to the question is: verifier output can replace ground-truth as the asymmetric signal, but every method that does so has to re-earn, somewhere else, the trust the answer key used to provide for free — through adversarial training, formal proof, or gating — or it inherits the verifier's blind spots.
Sources 7 notes
RLPR and INTUITOR successfully extend reinforcement learning for reasoning to general domains by using the model's own token probabilities and confidence levels as reward signals, eliminating the need for external verifiers or reference answers.
VeriFree bypasses answer verification entirely by using the conditional probability of reference answers given generated reasoning traces as both reward signal and training weight. This approach matches or surpasses verifier-based methods on MMLU-Pro, GPQA, and SuperGPQA without rule-based or model-based verifiers.
RARO uses an adversarial game where a critic discriminates expert from policy answers, eliminating the need for domain-specific verifiers while matching the scaling properties of verifier-based RL. The approach works across Countdown, DeepMath, and Poetry Writing tasks.
interwhen automatically generates code-based verifiers—including provably correct Lean and z3 checkers—from prose policy documents. This inverts the usual neuro-symbolic division: the LLM both translates policy to formal logic and extracts verifier inputs from reasoning traces.
Systems can add generated answers to their retrieval corpus when outputs pass entailment verification, source attribution checks, and novelty detection. This prevents hallucinations from polluting future retrievals while allowing genuine knowledge accumulation.
Decoupling verification from generation lets verifiers run alongside a single trace, forking to extract verifiable state and intervening only on violations. On correct runs the latency penalty is near-zero; interwhen matches or beats CoT across benchmarks at similar token budgets.
Logit lens analysis shows models trained with hidden CoT tokens compute correct answers in layers 1-3, then actively suppress these representations in final layers to produce format-compliant filler output. The reasoning is fully recoverable from lower-ranked token predictions.