CARE-PPO: RL Framework for LLM Quantitative Prediction with Confidence Estimation

Jul 15, 2026

Researchers present CARE-PPO, a reinforcement learning framework that adapts the actor-critic PPO method to enable large language models (LLMs) to jointly produce accurate numerical predictions and well-calibrated confidence estimates. By repurposing the critic network as a confidence estimator during inference, CARE-PPO demonstrates improved uncertainty calibration compared to logit-based and verbalized baselines on real-world healthcare and finance tasks. The approach also shows robustness to out-of-distribution data and reduces overfitting compared to supervised methods.

Why it matters: This work advances the reliability of LLMs in high-stakes domains by improving both prediction accuracy and confidence calibration, addressing a key challenge for trustworthy AI deployment.

Full story at: arXiv Computation and Language ↗