Curriculum Learning Teaches LLMs Long-Horizon Implicit Reasoning, Up To A Limit
Trains models to select the next step of a proof without writing intermediate reasoning, gradually increasing required lookahead and the number of attribute labels. Studies how training compute, model size, initialization, and curriculum steps affect learned lookahead and transfer to other reasoning tasks.
First public: · Added to this list: October 22, 2026
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Analysis and evaluation — Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.