跳到正文
研踪PaperTrail
← 研踪 PaperTrail

arXiv:2606.11270 · v3

Quantifying Subliminal Behavioral Transfer Ratios in Language Model Distillation

首次提交 2026-06-09 14:52
版本更新 2026-11-11 23:36
雷达观测 2026-11-13 14:16
arXiv ↗PDF ↗

摘要

Distillation of a language model intended to transfer benign behavior to a student model may also transfer undesirable characteristics, if they are present in the teacher model, a phenomenon known as subliminal learning. While qualitative evidence supports the existence of this effect, its magnitude has not been systematically characterized. This study quantifies subliminal behavioral transfer ratios by steering two teacher models (Llama-2-7B-Chat and Qwen2.5-7B-Instruct) at varying steering strengths and distilling student models using only benign data. Evaluation on 100 JailbreakBench prompts with GPT-4.1, serving as the evaluator, indicates that transfer is robust but exhibits distinct scaling behaviors. Llama-2 demonstrates a sharp threshold (τ=0.25,0.32 beyond α=−0.15τ= {0.25,0.32} \ \text{beyond} \ α= -0.15), whereas Qwen2.5 displays continuous and higher levels of transfer (ττ up to 0.610.61).

作者备注:Accepted at the Mechanistic Interpretability Workshop at ICML 2026

AI 论文解读

基于摘要 · 非全文审阅

此版本尚未生成 AI 解读。解读由后台任务生成并按论文版本保存,登录后共享已有结果。

版本差异

本站首次收录为 v3,暂无更早版本可对照。

观测到的版本

相邻记录可能跳过中间版本;首次发现 v2 及以上也会标为版本更新。

v3 · 2026-11-11 23:36 · 最新观测版本

Quantifying Subliminal Behavioral Transfer Ratios in Language Model Distillation

Distillation of a language model intended to transfer benign behavior to a student model may also transfer undesirable characteristics, if they are present in the teacher model, a phenomenon known as subliminal learning. While qualitative evidence supports the existence of this effect, its magnitude has not been systematically characterized. This study quantifies subliminal behavioral transfer ratios by steering two teacher models (Llama-2-7B-Chat and Qwen2.5-7B-Instruct) at varying steering strengths and distilling student models using only benign data. Evaluation on 100 JailbreakBench prompts with GPT-4.1, serving as the evaluator, indicates that transfer is robust but exhibits distinct scaling behaviors. Llama-2 demonstrates a sharp threshold (τ=0.25,0.32 beyond α=−0.15τ= {0.25,0.32} \ \text{beyond} \ α= -0.15), whereas Qwen2.5 displays continuous and higher levels of transfer (ττ up to 0.610.61).

查看此版本解读 →查看此版本原文 ↗