Papers by Xingzhou Lou
Calibration-Aware Policy Optimization for Reasoning LLMs (2026.acl-long)
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| Challenge: | Existing approaches to model calibration are limited or sacrifice gains in reasoning accuracy. |
| Approach: | They propose a method that improves calibration by 15% while boosting accuracy by 5% . they propose GRPO-style algorithms that misalign uncertainty-agnostic advantage estimation . |
| Outcome: | The proposed approach improves calibration by 15% while achieving comparable to or better than GRPO on multiple mathematical reasoning benchmarks. |