Task-Specific Knowledge Distillation via Intermediate Probes

Researchers introduce a new knowledge distillation framework that improves training of smaller AI models by using intermediate representations from large language models rather than their final outputs. The method shows consistent improvements across reasoning benchmarks, particularly when training data is limited, by providing cleaner supervision signals.

• →New distillation method bypasses noisy output bottlenecks by training probes on frozen teacher model hidden states.
• →Technique shows consistent improvements across four reasoning benchmarks with gains most pronounced under limited data conditions.
• →Method requires no architectural changes and adds minimal computational overhead since probe training is inexpensive.
• →Intermediate representations provide cleaner labels than teacher outputs, effectively denoising the distillation signal.
• →Framework is architecture-agnostic and enables extracting more value from large models without additional training data.