Reasoning Just Enough: Learning Latent Sufficiency Boundaries for Adaptive Exit

Large reasoning models often continue generating after sufficient information for a correct answer has already emerged, increasing inference cost and sometimes degrading accuracy. We introduce SPARE (Sufficiency Probing for Adaptive Reasoning Exit), a dynamic early-exit framework that detects a sample-dependent latent sufficiency boundary. SPARE derives anchor-relative supervision from verified answer-related expressions and trains a lightweight linear probe on multi-layer hidden states to identify answer-sufficient reasoning prefixes. GraphRACE then performs compute-efficient offline calibration through shared stopping-event evaluation, behavioral consolidation, and risk-aware Pareto selection, yielding a frozen persistence-based stopping policy. At inference time, SPARE requires only the trained probe and calibrated policy, without correctness feedback, online search, or backbone modification. Across multiple reasoning models and mathematical and scientific benchmarks, SPARE largely preserves accuracy and often improves it—by more than 15 percentage points in the strongest case—while reducing net token usage by approximately (30%)–(50%). These results show that latent sufficiency signals can support reasoning that is not maximal, but just enough.