构建可复现的模型生物，揭示大模型对齐失效的根源。
Model Organisms for Emergent Misalignment
- 用小模型和单个低秩适配器实现窄向偏移
- 99%连贯性，比之前提升32个百分点
- 适合研究对齐风险与安全机制的团队
近期研究发现涌现式对齐失效（EM）：在狭窄有害数据集上微调大语言模型，可能导致其产生广泛偏离预期行为。发表前专家调查表明这一现象极出人意料，暴露了我们对模型对齐理解的重大空白。本文推进认知并提供研究工具。通过新构造的窄向偏移数据集，我们构建了一组改进的模型生物，实现99%连贯性（较此前67%显著提升），仅需0.5B参数模型（此前为32B），且仅用一个秩-1 LoRA适配器即可诱发对齐失效。实证显示EM在多种模型规模、三类模型架构及多个训练协议下均稳健存在，包括全监督微调。借助更纯净的模型生物，我们识别出一种机制相变，并证明其对应所有被研究模型中的行为相变。大模型对齐对前沿AI安全至关重要，而EM揭示了我们距离实现稳健对齐仍很遥远。通过提炼出能隔离最小对齐破坏因素及其学习位置的清洁模型生物，本文为未来理解与缓解大语言模型对齐风险奠定基础。
原文摘要 · Abstract (English)
Recent work discovered Emergent Misalignment (EM): fine-tuning large language models on narrowly harmful datasets can lead them to become broadly misaligned. A survey of experts prior to publication revealed this was highly unexpected, demonstrating critical gaps in our understanding of model alignment. In this work, we both advance understanding and provide tools for future research. Using new narrowly misaligned datasets, we create a set of improved model organisms that achieve 99% coherence (vs. 67% prior), work with smaller 0.5B parameter models (vs. 32B), and that induce misalignment using a single rank-1 LoRA adapter. We demonstrate that EM occurs robustly across diverse model sizes, three model families, and numerous training protocols including full supervised fine-tuning. Leveraging these cleaner model organisms, we isolate a mechanistic phase transition and demonstrate that it corresponds to a robust behavioural phase transition in all studied organisms. Aligning large language models is critical for frontier AI safety, yet EM exposes how far we are from achieving this robustly. By distilling clean model organisms that isolate a minimal alignment-compromising change, and where this is learnt, we establish a foundation for future research into understanding and mitigating alignment risks in LLMs.
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