Adaptive Generate-Rank-Verify: Inference-Time Search with Costly Verification
Many inference-time language-model pipelines combine a cheap reward signal with an expensive verifier, such as exact answer checking in mathematical reasoning or hidden-test execution in code generation. We formalize this setting using a learning-theoretic lens as generative active search: a cost-sensitive first-positive search problem in which a policy adaptively samples candidates from an unknown distribution, observes cheap scores, and pays for verifier labels until it finds a positive example. For a fixed prompt, the generator and reward model induce two unknown objects: a distribution over reward scores and a score-conditioned success function. When these quantities are known, we cha...
| Authors | Shaddin Dughmi, Mahdi Haghifam, Yusuf Hakan Kalayci |
|---|---|
| Primary category | cs.LG |
| Published | 17 May 2026 |
| Last updated at source | 7 June 2026 |
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