Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note2 min read

From Critic to Confidence: PPO for Language-Based Quantitative Prediction with Confidence Estimation

Source

arXiv

arxiv.org/abs/2607.12687v1

Read the source

Source

Plain-Language Brief

Researchers introduce a new framework called CARE-PPO to improve language-based quantitative prediction by both accurately estimating numerical values and providing reliable confidence signals. The method uses reinforcement learning and a Confidence-Aligned Reward for Estimation, which provides feedback to the model during training, and repurposes the critic as a confidence estimator during inference. The study shows that CARE-PPO outperforms other baselines across various tasks and scales in healthcare, finance, and general instruction-following prompts.

What the Document Says

  • LLMs can perform language-based quantitative prediction but are susceptible to errors.
  • CARE-PPO is a reinforcement learning framework that combines loss prediction for uncertainty estimation with actor-critic PPO fine-tuning.
  • CARE-PPO uses a Confidence-Aligned Reward for Estimation as a feedback mechanism during training.
  • During inference, the critic in CARE-PPO is repurposed as a confidence estimator.
  • CARE-PPO has been tested on two real-world tasks in healthcare and finance, and two Qwen-3 model scales (4B and 8B).
  • CARE-PPO achieves strong quantitative prediction performance and better-aligned confidence estimates than logit-based and verbalized baselines.
  • These improvements persist under realistic out-of-distribution settings across domains, spanning linguistic and domain shifts.
  • CARE-PPO reduces task-specific overfitting on general instruction-following prompts.

Key Concepts Mentioned

  • Language-based quantitative prediction
  • Hallucinations
  • Overconfident errors
  • Reinforcement learning
  • Actor-critic PPO fine-tuning
  • CARE-PPO
  • Confidence-Aligned Reward for Estimation
  • Healthcare
  • Finance

Claims Made by the Document

  • CARE-PPO achieves strong quantitative prediction performance.
  • CARE-PPO produces better-aligned confidence estimates than logit-based and verbalized baselines.
  • These improvements persist under realistic out-of-distribution settings across domains.

Evidence Used

  • Results from experiments on two real-world tasks in healthcare and finance.

Methods / Approach

  • Reinforcement learning
  • Actor-critic PPO fine-tuning
  • Confidence-Aligned Reward for Estimation

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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 the actor while inducing the critic to learn a value function aligned with prediction quality. During inference, we repurpose the critic as a confidence estimator. Across two real-world tasks in healthcare and finance and two Qwen-3 model scales (4B and 8B), CARE-PPO achieves strong quantitative prediction performance, while producing significantly better-aligned confidence estimates through the critic than logit-based and verbalized baselines. These gains persist under realistic out-of-distribution settings across domains, spanning linguistic and domain shifts. Finally, CARE-PPO reduces task-specific overfitting on general instruction-following prompts, consistent with the broader generalization advantages of RL fine-tuning over supervised approaches.