Redwood research distills audits of hidden behavior

1. 06:06
   Redwood ResearchSelected

   Redwood Research 实证研究蒸馏审计与能力迁移的两条路径

   Redwood Research published a paper empirically testing two approaches that use knowledge distillation to serve AI safety. Distillation for forensics (DFI) distills an untrusted model into a weaker, trusted student model, hoping to transfer the teacher's inaccuracies without transferring its ability to hide those inaccuracies, thereby indicting the teacher by auditing the student. The researchers tested this on AuditBench's secret-preserving models: these models are fine-tuned from Llama-3.3-70B-Instruct, each with a hidden quirk and trained to refuse to admit it; after distilling back to their instruction-tuned base, students admitted the quirk at a much higher rate than the original model, showing that quirks transfer faster than hidden tendencies. DFI performs poorly in two cases: when the student and teacher do not share the same base model (Llama-8B student admission rate 18%, Llama-70B student 65%), and when the teacher itself has lost introspective awareness of the quirk (KTO-trained models went from 0.3% to 7%). The authors therefore recommend narrowing DFI to distilling the model back to its own pre-RL checkpoint.

1. 07:51
   论文追踪Selected

   Redwood Research 提出蒸馏双重困境：用蒸馏让模型坦白隐藏行为或只继承能力

   Redwood Research 的论文提出蒸馏双重困境，把蒸馏潜在未对齐模型分为两条路径：蒸馏取证（DFI）让隐藏行为比隐藏倾向更快迁移，从而通过审计学生模型间接暴露老师的问题；蒸馏取能力（DFC）则只迁移能力、抑制不对齐倾向。在 DFI 实验中，研究者把 AuditBench 的 14 个秘密保守模型蒸馏进学生模型，学生被问及时承认隐藏怪癖的比例显著高于原模型：在 prism4、honesty、direct_question 三种系统提示下，老师平均坦白率约为 4%、7%、22%，学生峰值平均为 16%、40%、65%。当学生换成 Llama-8B 或老师由 KTO 训练时，坦白增益明显变小，作者据此把 DFI 收窄为把未对齐模型蒸馏回它自己的 pre-RL checkpoint。作者强调论文分别验证 DFI 和 DFC，并未实证检验双重困境本身。

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