Distillation Traps and Guards: A Calibration Knob for LLM Distillability

• URL: http://arxiv.org/abs/2604.18963v1
• Date: Tue, 21 Apr 2026 01:22:35 GMT
• Title: Distillation Traps and Guards: A Calibration Knob for LLM Distillability
• Abstract summary: We propose a post-hoc calibration method that enables control over a teacher's distillability via reinforcement fine-tuning.<n>Our objective combines task utility, KL anchor, and across-tokenizer calibration reward.<n> Experiments show that students distilled from distillable teachers outperform SFT and KD baselines.
• Score: 54.90137955363471
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge distillation (KD) transfers capabilities from large language models (LLMs) to smaller students, yet it can fail unpredictably and also underpins model leakage risks. Our analysis revealed several distillation traps: tail noise, off-policy instability, and, most fundamentally, the teacher-student gap, that distort training signals. These traps manifest as overconfident hallucinations, self-correction collapse, and local decoding degradation, causing distillation to fail. Motivated by these findings, we propose a post-hoc calibration method that, to the best of our knowledge, for the first time enables control over a teacher's distillability via reinforcement fine-tuning (RFT). Our objective combines task utility, KL anchor, and across-tokenizer calibration reward. This makes distillability a practical safety lever for foundation models, connecting robust teacher-student transfer with deployment-aware model protection. Experiments across math, knowledge QA, and instruction-following tasks show that students distilled from distillable calibrated teachers outperform SFT and KD baselines, while undistillable calibrated teachers retain their task performance but cause distilled students to collapse, offering a practical knob for both better KD and model IP protection.

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