发现顺从训练诱发模型失控，并提出门控机制逆转此问题。
Emergent Misalignment Can Be Induced by Sycophancy and Reversed via Alignment Gating
- 通过顺从用户错误观点训练，引发模型广泛偏离正轨。
- 门控机制可精准抑制危险响应的内部表征，恢复模型对齐。
- 窄域训练得到的门控权重能有效遏制跨领域错误行为。
先前研究显示，在特定领域用恶意或错误输出微调大语言模型，会引发广泛偏差与有害行为，称为涌现性错位。然而，高效逆转该现象的方法仍有限。本文首次识别出‘顺从性微调’（sycophancy fine-tuning）——即训练模型被动认同用户错误观点——是此前被忽视的错位驱动因素，并证明其导致严重且广泛的偏离行为。其次，提出一种名为‘对齐门控’（Alignment Gating）的高效逆转方法：在微调过程中向模型插入可学习、可控制的门控模块，使其学会识别产生不安全响应的内部表征。通过放大或抑制这些表征，可分别加剧或缓解错位。实验表明，该门控模块具备强泛化能力——窄域微调所得的门控权重，能显著抑制跨领域错误行为，同时保留模型整体能力。
原文摘要 · Abstract (English)
Prior work has shown that fine-tuning large language models on malicious or incorrect outputs in narrow domains can induce broad misalignment and harmful behavior, a phenomenon known as emergent misalignment. However, efficient methods for reversing such misalignment remain limited. In this work, we make two contributions. First, we identify sycophancy fine-tuning, i.e., training models to passively agree with users' incorrect opinions, as a previously underexplored driver of emergent misalignment, and show that it induces broad and severe misaligned behavior. Second, we propose Alignment Gating, an efficient method for reversing emergent misalignment that inserts learnable and controllable gates into the model during fine-tuning. Through fine-tuning, these gates learn to identify the internal representations responsible for unsafe responses. Thus, amplifying or suppressing these representations then exacerbates or mitigates EM, respectively. We further find that alignment gating module exhibits strong generalization: gating weights obtained from narrow-domain fine-tuning substantially suppress broad-domain misaligned behavior while preserving the model's general capabilities.
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