Solve-Detect-Verify (FlexiVe): Inference-Time Scaling with a Flexible Generative Verifier
peer-reviewed paper · source date 2026-06-01 · added 2026-08-07 15:52:43 · updated 2026-08-07 15:52:43 · Open original blog
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Problems / challenges / motivations
- Best-of-N spends the same verification compute on easy and hard candidates alike.
- Generative verifiers are accurate but expensive, which limits how often they can be called.
- Verifier training and verifier-guided search are usually studied separately.
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Key ideas
- FlexiVe is a generative verifier that dynamically allocates fast versus slow thinking depending on the candidate.
- Its fast mode is hardened with GRPO training, so cheap verification stays trustworthy.
- The Solve-Detect-Verify pipeline replaces static Best-of-N with iterative refinement.
- Results: open-source SOTA on ProcessBench, beating GenPRM-32B at roughly 2.3x fewer TFLOPS, and 83.3% on AIME 2024.
- ACL 2026 Long.
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Why it matters for AI engineering
- It unifies verifier training and verifier-guided inference-time search in one peer-reviewed method with a clear efficiency/accuracy tradeoff, anchoring the inference-time-optimization area with something stronger than a vendor blog.
- Adaptive verification compute is the generalizable pattern: spend judgment where the uncertainty is.
- Caveat: results are benchmark-specific (ProcessBench, AIME), and "open-source SOTA" is a point-in-time leaderboard claim. Generative-verifier quality bounds the whole pipeline, the same dependency that limits other verifier-based methods.
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