通过对抗分支探测实现稳定推理提前退出，显著降低计算成本。
SABER: Stability-Aware Early Exit for LLM Reasoning via Adversarial Branch Probing
- 构建语义扰动分支，轻量探测未来结果以判断是否稳定
- 平均减少30.2%~39.8%的推理令牌消耗，精度不降
- 无需训练，适配多种模型与推理任务
大型推理模型虽具强推理能力，但一旦中间答案在推理步骤中趋于稳定，继续推理将带来极小边际收益却增加大量计算开销。现有基于置信度或熵的早退方法难以捕捉推理稳定性，而一致性方法依赖多步轨迹一致，需顺序评估导致延迟。为此，我们提出SABER——一种无需训练的稳定性感知早退框架，通过在中间推理状态周围构造简单有效的语义扰动形成对抗分支，并用轻量探测估计其可能的最终结果，无需完整轨迹回滚。当探测结果在各分支间保持一致时，系统提前退出；否则继续推理。在多个推理基准和模型架构上的实验表明，SABER平均降低30.2%–39.8%的推理令牌消耗，同时保持与完整推理相当的精度。
原文摘要 · Abstract (English)
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-aware early exit via adversarial branch probing. SABER constructs simple yet effective semantic perturbations around intermediate reasoning states to form adversarial branches, and applies lightweight probing to estimate their likely final outcomes without full trajectory rollouts. When the probed outcomes remain consistent across branches, SABER exits early; otherwise, it continues reasoning. Experiments across multiple reasoning benchmarks and model architectures show that SABER reduces reasoning token consumption by 30.2\%--39.8\% on average while maintaining competitive accuracy with full-length reasoning.
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