SMDA fits ridge regression on SAE features to distill symbolic policies then decomposes each SFT example's influence via feature-activation and output-probability deltas, demonstrated on refusal behavior in Llama-3.2-3B-Instruct.
arXiv preprint arXiv:2509.25002 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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Reasoning in large output spaces proceeds via shortlisting then fine-grained reasoning; this characterization enables a mechanistic distillation strategy that outperforms standard distillation.
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Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces
Reasoning in large output spaces proceeds via shortlisting then fine-grained reasoning; this characterization enables a mechanistic distillation strategy that outperforms standard distillation.