Large Decision Models: Repurposing Large Language Models as Single-Pass Decision Makers
Trains a language model to score labels supplied at inference in one forward pass, without generating a reasoning trace. Distills label distributions from repeated teacher reasoning and studies routing uncertain decisions back to explicit reasoning.
First public: · Added to this list: October 18, 2026
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arXiv:2610.15501 · Initial submission: 2026-10-11
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Internalized reasoning — Learn from worked solutions so that some or all of the reasoning can later happen without writing those steps. Training may use a separate teacher model or progressively remove written steps from the model being trained.
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