Assess whether TailRL preserves accuracy during calibration optimization

Determine whether optimizing calibration with TailRL preserves more task-solving accuracy than the RLCR calibration objective for language-model reasoning.

Background

The experiments show that RLCR can continue improving its Brier-score-based calibration reward after answer accuracy has peaked and begun to decline. The authors hypothesize that a likelihood-based objective such as TailRL could avoid or reduce this accuracy tradeoff, but they do not test that possibility in the paper. The unresolved issue concerns whether TailRL can improve calibration while retaining more mathematical problem-solving accuracy than RLCR.

References

We hypothesize that optimizing calibration with methods such as TailRL could preserve more accuracy, and leave for future work.

— Training Language Models to Fail Gracefully  (2610.20401 - Arora et al., 14 Oct 2026) in Appendix, subsection “RLCR Can Trade Accuracy for Calibration”