Imprinting: Knowledge Distillation Can Shift Student Behavior
cs.CL
Submitted: 2026-10-13
Updated: 2026-10-13
License: http://creativecommons.org/licenses/by/4.0/
The gist: Knowledge distillation trains student models on teacher-generated responses to transfer task capability, but those responses may also carry behavioral tendencies of their source.
Terminology
Abstract
Knowledge distillation trains student models on teacher-generated responses to transfer task capability, but those responses may also carry behavioral tendencies of their source. We investigate whether the source teacher can shape student behavior beyond the capability that distillation is intended to transfer. We construct harm-accepting and harm-avoiding variants of the same base teacher, use them to generate responses to ordinary reasoning and knowledge questions unrelated to safety, and distill those responses into students with different base models. Students generally acquire task capability while their downstream decisions shift toward the source teacher's safety-pragmatism disposition, a phenomenon we call imprinting. Distillation from the harm-accepting teacher reduces harm avoidance, whereas distillation from the harm-avoiding teacher increases it. Imprinting varies across teacher-student pairs and does not extend uniformly across different forms of safety behavior. Excluding a sparse set of divergence tokens reduces imprinting, whereas response rewriting preserves the source-dependent effect and can introduce additional rewriter-dependent shifts. Post-distillation preference optimization raises harm avoidance but does not eliminate the source-dependent difference. These results show that behavioral tendencies can be intentionally imprinted through the source teacher, even with ordinary task supervision.
Sources
- Pay Attention to the Triggers: Constructing Backdoors That Survive Distillation
- You Didn't Have to Say It like That: Subliminal Learning from Faithful Paraphrases
- Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training
- Quantifying Subliminal Behavioral Transfer Ratios in Language Model Distillation
- NaturalThoughts: Selecting and Distilling Reasoning Traces for General Reasoning Tasks
- Channel Location Constrains the Auditability of Subliminal Learning
- Subliminal Steering: Stronger Encoding of Hidden Signals
- Breaking the Tokenizer Barrier: On-Policy Distillation across Model Families
- Granite Guardian
- Covert Influence Between Language Models
- Convergent Linear Representations of Emergent Misalignment
- Qwen2.5 Technical Report
- Qwen3 Technical Report
- Qwen3Guard Technical Report
- Instruction-Following Evaluation for Large Language Models
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