Uncertainty-Guided Progressive Contraction for efficient reasoning post-training (arXiv:2610.12933v1)
The paper proposes an uncertainty-guided progressive contraction framework that compresses reasoning supervision and focuses optimization on high-value reasoning trajectories. It builds Monte Carlo reasoning trees around intermediate CoT states to profile uncertainty, first contracts a teacher-informed training pool and then performs learner-aware online routing during SFT, claiming up to ~450x less training exposure (as little as 0.22% of full-data SFT) while maintaining or improving reasoning across benchmarks and diverse LLM families.