From Critic to Confidence: PPO for Language-Based Quantitative Prediction with Confidence Estimation
LLMs can perform language-based quantitative prediction from unstructured inputs, but remain susceptible to hallucinations and overconfident errors, making it critical to know not only what a model predicts, but when its predictions can be trusted. We introduce CARE-PPO, a reinforcement learning framework that establishes a connection between loss prediction for uncertainty estimation and actor-critic PPO fine-tuning, enabling joint learning of accurate numerical estimates and reliable confidence signals in language-based quantitative prediction. CARE-PPO uses a Confidence-Aligned Reward for Estimation, defined as a function of prediction error, to provide dense error-aware feedback to th...
| Authors | Mehak Dhaliwal, Rasta Tadayon, Andong Hua et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 July 2026 |
| Last updated at source | 14 July 2026 |
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