Task-Specific Knowledge Distillation via Intermediate Probes
This work addresses a bottleneck in knowledge distillation for reasoning tasks, enabling practitioners to extract more value from large teacher models without extra data or architectural changes, though it is incremental as it builds on existing distillation methods.
The paper tackles the problem of noisy teacher outputs in knowledge distillation for reasoning tasks by introducing a framework that uses intermediate hidden states to train lightweight probes, which then provide cleaner supervision for student models, achieving consistent improvements across four reasoning benchmarks, especially under limited data conditions.
Knowledge distillation from large language models (LLMs) assumes that the teacher's output distribution is a high-quality training signal. On reasoning tasks, this assumption is frequently violated. A model's intermediate representations may encode the correct answer, yet this information is lost or distorted through the vocabulary projection, where prompt formatting and answer-token choices creates brittle, noisy outputs. We introduce \method{}, a distillation framework that bypasses this bottleneck by training lightweight probes on frozen teacher hidden states and using the probe's predictions, rather than output logits, as supervision for student training. This simple change yields consistent improvements across four reasoning benchmarks (AQuA-RAT, ARC Easy/Challenge, and MMLU), with gains most pronounced under limited data. Probes trained on intermediate representations provide cleaner labels than the teacher's own outputs, effectively denoising the distillation signal. \method{} requires no architectural changes to student or teacher, is architecture-agnostic, and adds minimal compute since probe training is cheap and teacher representations can be cached. By exploiting internal representations, \method{} enables practitioners to extract more value from large teacher models without additional training data or architectural complexity.