SOURCE-LINKED INTELLIGENCE

SABER: Stability-Aware Early Exit for LLM Reasoning via Adversarial Branch Probing

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

Large Reasoning Models (LRMs) achieve strong reasoning capabilities, yet long-chain reasoning becomes inefficient once the intermediate answer stabilizes across reasoning steps: additional reasoning yields little marginal benefit while incurring substantial inference cost. Existing early-exit methods based on confidence or entropy poorly capture reasoning stability, while consistency-based approaches rely on multi-step trajectory agreement, requiring sequential evaluations that delay exit. To better balance efficiency and reliability, we propose SABER, a training-free framework for stability-a

Read original source ↗ Open in workspace

recordType

paper

region

Global