发现思维链模型的推理成败可早期预测，提升效率与准确性。
Token Budget Saturation and Mechanistic Early Detection of Reasoning Non-Convergence in Chain-of-Thought Models
- 用中间层激活值构建线性探测器，提前判断推理是否能完成。
- 在第150个词元时，准确率可达90.3%，非收敛生成仅6.6%。
- 适合需要高效推理和动态计算分配的场景，如大模型部署。
Chain-of-thought推理模型如DeepSeek-R1-Distill-Qwen-7B表现出二态收敛模式：生成要么在词元预算内结束（收敛），要么耗尽预算仍未得出结论（非收敛）。实证显示，收敛生成在AIME 1983–2024数据集上准确率达90.3%，而非收敛生成仅6.6%，整体收敛率为62.0%。我们进一步探究能否通过内部表示提前识别该结果。在词元位置50–300处训练线性探测器，发现第20层在词元150时达到AUC 0.608（±0.080，5折交叉验证），即使在词元50时也显著高于随机水平。激活值探测器始终优于基于词元熵和重复统计的行为基线。全范围置换检验p=0.063（100,000次置换），表明存在微弱信号，样本量尚不足以在传统阈值下确认。结果表明，收敛命运部分编码于生成结束前的中间表示中，为早期退出推理与自适应计算分配提供了可能路径。
原文摘要 · Abstract (English)
Chain-of-thought reasoning models such as DeepSeek-R1-Distill-Qwen-7B exhibit a bimodal convergence pattern: generations either terminate within a token budget (converged) or exhaust it without reaching a conclusion (non-converged). We characterize this phenomenon empirically, showing that converged generations achieve 90.3% accuracy on AIME 1983-2024 while non-converged ones achieve only 6.6%, with an overall convergence rate of 62.0%. We then ask whether this outcome is detectable early in the thinking chain using internal model representations. Training linear probes on hidden-state activations at token positions 50-300, we find that layer-20 activations at token 150 achieve AUC 0.608 (+-0.080, 5-fold CV), reliably above chance even at token 50. Activation probes consistently outperform behavioral baselines derived from token entropy and repetition statistics. A sweep-level permutation test yields p=0.063 (100,000 permutations), consistent with a modest signal that our sample size cannot confirm at conventional thresholds. These findings suggest that convergence fate is partially encoded in intermediate representations well before the generation ends, opening a path toward early-exit inference and adaptive compute allocation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。