Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces

Abstract:Modern reasoning models offer surprisingly strong zero-shot performance on challenging multi-label tasks that require selecting a small set of relevant options from hundreds of thousands to millions of candidate labels. We investigate how they achieve this mechanistically. We characterize reasoning as a two-phase process: A broad "shortlisting" of candidates followed by fine-grained reasoning over the resulting set. We provide evidence across a range of datasets that these steps can be isolated and are complementary. Using this characterization, we develop a mechanistic distillation strategy that consistently outperforms standard distillation.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2606.06840 [cs.CL]

(or arXiv:2606.06840v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.06840 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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