Less Approximates More: Harmonizing Performance and Confidence Faithfulness via Hybrid Post-Training for High-Stakes Tasks

Haokai Ma, Lee Yan Zhen, Gang Yang, Yunshan Ma, Ee-Chien Chang et al.|April 9, 2026arXiv

Key Takeaway

For high-stakes AI applications, you can improve both accuracy and confidence calibration by smartly combining supervised reasoning examples with unsupervised learning, rather than treating them separately.

Summary

This paper addresses a critical problem in AI safety: large language models that are confidently wrong in high-stakes applications.

safety training reasoning

Key Terms

confidence-calibration reasoning-trace reinforcement-learning-from-internal-feedback reasoning-distillation