论文发现，用模型早期检查点的解释训练自己，它给出的解释反而更忠实于当前行为，能追踪行为变化，挺神奇的。
论文发现，用固定反事实解释训练语言模型（LM）时，产生的解释更忠实于当前行为而非训练目标。该现象在谄媚（sycophancy）和拒绝（refusal）等任务中出现。即使同时进行其他后训练目标，解释也能追踪行为偏移。结果表明，固定数据集的反事实解释可作为可扩展的后训练信号。
Introspective Coupling: Self-Explanation Training Tracks Behavioral Change Despite Fixed Supervision
When does training language models (LMs) to generate explanations of their predictions yield faithful introspection, rather than superficial imitation? We study LMs trained to explain which features of their inputs influenced their behavior, using models' counterfactual behavior on modified inputs as supervision. Surprisingly, we find that LMs trained on fixed counterfactual explanations derived from earlier checkpoints of themselves, or even from behaviorally similar models in different families, frequently produce explanations more faithful to their own current behaviors than to those of their training targets. This "introspective" coupling between LM explanations and behaviors occurs when training explanations remain sufficiently correlated with current behaviors over the course of training, even as behaviors themselves shift. We also show that introspective coupling tracks behavior shifts: when explanation training is provided concurrently with other post-training objectives, explanations track those shifts without requiring updated supervision. This phenomenon appears in multiple tasks, including sycophancy and refusal, and is robust to label noise. Overall, our results show that even fixed datasets of counterfactual explanations can provide scalable and generalizable post-training signal for introspection.