Valid Stopping for LLM Generation via Empirical Dynamic Formal Lift
- URL: http://arxiv.org/abs/2510.06478v1
- Date: Tue, 07 Oct 2025 21:28:53 GMT
- Title: Valid Stopping for LLM Generation via Empirical Dynamic Formal Lift
- Abstract summary: Sequential-EDFL reduces generation by 22-28% vs. sequential baselines.<n>EDFL serves as a first-stage filter reducing verification burden by 83%.
- Score: 6.908972852063454
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce Sequential-EDFL (Empirical Dynamic Formal Lift), applying anytime-valid sequential testing to language model generation stopping. Our approach tracks information lift -- the log-likelihood ratio between full models and deliberately weakened "skeleton" baselines -- using self-normalized empirical-Bernstein e-processes that provide formal delta-level error control regardless of stopping time. We handle unknown centering through online mean estimation, combine multiple parameters via mixture e-processes, and support adaptive resets under distributional drift. On six benchmarks, Sequential-EDFL reduces generation by 22-28% vs. sequential baselines while maintaining delta-level control with 12% computational overhead. We introduce automated skeletons (distilled submodels, randomized logits) and show robustness across skeleton families. Composing EDFL with a lightweight correctness gate (sentence boundaries + verifier) improves end-task correctness while preserving anytime-valid guarantees by only delaying stopping. Our certificates control information sufficiency, not factual correctness -- 10.9% of stopped sequences remain incorrect even with the gate (13.2-22.7% without it). EDFL serves as a first-stage filter reducing verification burden by 83%, not as a standalone solution for safety-critical domains.
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