care-ppo-6a5d79f4·1 events·first seen Aliases: CARE-PPO
Researchers introduce CARE-PPO, a reinforcement learning fine-tuning framework that jointly trains LLMs for numerical prediction accuracy and calibrated confidence estimation. The approach repurposes the PPO critic as a confidence estimator at inference time, using a Confidence-Aligned Reward for Estimation derived from prediction error. Evaluated on healthcare and finance tasks with Qwen-3 4B and 8B models, CARE-PPO outperforms logit-based and verbalized confidence baselines and shows improved out-of-distribution generalization. The work addresses the hallucination and overconfidence problems that limit LLM deployment in high-stakes quantitative domains.