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arXiv AI By Suorong Yang, Jiaxin Wang, Jiaming Su, Chentao Yue, Furao Shen, Peng Ye, Soujanya Poria

Learning More by Training Less: Uncertainty-Guided Progressive Contraction for Efficient Reasoning Post-Training

Read the original on arXiv AI →

The paper introduces an uncertainty-guided progressive contraction framework to reduce the amount of reasoning supervision needed for large language models. By building Monte Carlo reasoning trees around intermediate states, the method contracts the training corpus into a compact teacher-informed pool and then performs learner-aware contraction during supervised fine-tuning. Experiments show that the approach can use as little as 0.22% of the optimization budget of full-data SFT while maintaining or improving reasoning performance across diverse benchmarks and model families.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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