Teaching Small LMs to Reason
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An approach that teaches smaller language models to explicitly select among reasoning techniques for each problem.
Reasoning technique menu: Trains the small LM to choose among step-by-step processing, recall-then-generate, recall-reason-generate, extract-generate, and direct-answer strategies.
Technique selection: The model learns when to apply each strategy based on problem structure, not just which answer to produce.
Matches 5-10x larger models: Attains zero-shot reasoning performance similar or better than models 5-10x larger on complex reasoning tasks.
Practical scaling: Offers a recipe for teams that can't deploy frontier-scale models but need strong reasoning quality - a recurring production constraint.
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