# Uncertainty-Guided Progressive Contraction

Reasoning post-training no longer feeds every teacher chain-of-thought to the student at equal weight. Scaling the corpus had raised training cost without proportional gains. Here Monte Carlo reasoning trees estimate process uncertainty at intermediate states to contract the corpus into a teacher-informed pool, and online routing during supervised fine-tuning concentrates updates on what the student has not yet learned, cutting the optimization budget to 0.22–1.91% of full-data SFT across reasoning benchmarks and model families while matching or improving performance.

Reasoning post-training no longer feeds every teacher chain-of-thought to the student at equal weight. Scaling the corpus had raised training cost without proportional gains. Here Monte Carlo reasoning trees estimate process uncertainty at intermediate states to contract the corpus into a teacher-informed pool, and online routing during supervised fine-tuning concentrates updates on what the student has not yet learned, cutting the optimization budget to 0.22–1.91% of full-data SFT across reasoning benchmarks and model families while matching or improving performance.