Researchers Identify and Address Context-Induced Degradation in AI Model Distillation
A new arXiv preprint identifies a previously unstudied failure mode in on-policy knowledge distillation, where reintroducing privileged context to a distilled student model degrades its performance even on tasks it already handles correctly without that context. The authors term this phenomenon 'context-induced degradation' and argue that robust internalization requires not just matching teacher behavior but also remaining stable when context is reintroduced — a property they call 'context removability.' They propose a lightweight consistency regularizer that mitigates this issue across 11 of 12 tested configurations while also reducing response-length inflation.
On-policy distillation is a technique in which a student model is trained to internalize privileged information — such as system prompts or task hints — so that the information is no longer needed at inference time. While prior work has shown this improves no-context performance, the authors of this preprint reveal a counterintuitive side effect: when the original context is reintroduced to the distilled student, performance often drops, even on instances the student already solves correctly without it. To address this, the paper introduces a consistency regularizer that anchors the student's no-context output via stop-gradient and penalizes the context-conditioned output for deviating from it using forward KL divergence. The method requires only one additional forward pass per training step, making it computationally lightweight. Tested across 12 configurations spanning multiple domains and model families, the approach improves context-conditioned accuracy in the majority of settings, reduces context-induced harm in 11 out of 12 cases, and eliminates response-length inflation. A mechanistic analysis further shows that context removability is achieved at the representation level, with hidden states remaining nearly identical whether or not context is present. The paper was submitted to arXiv on June 10, 2026, and has not yet undergone peer review.
What's missing
As a preprint, this work has not been peer-reviewed. Key open questions include whether the consistency regularizer generalizes to much larger model scales, whether the stop-gradient anchoring strategy could suppress beneficial context in some settings, and how the method performs when privileged context is qualitatively different from what was seen during distillation training.
What different sources said
- arXiv cs.AICenter
When Context Returns: Toward Robust Internalization in On-Policy Distillation
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