Distribution-Aware Reward: Reinforcement Learning over Predictive Distributions for LLM Regression
arXiv:2605.20740v1 Announce Type: new Abstract: Large language models can predict real-valued quantities from heterogeneous inputs such as text, code, and molecular strings, but most training objectives score each decoded floating-point number independently, improving point estimates without ensuring calibrated predictive distributions. This limits applications requiring candidate ranking or uncertainty estimation. We introduce Distribution-Aware Reward, an on-policy reinforcement learning objective whose main contribution is to train language models to produce better predictive distributions
The increasing sophistication of LLMs and their application to complex, real-world regression tasks necessitates better methods for uncertainty quantification and robust predictive capabilities beyond simple point estimates.
This research addresses a critical limitation in current LLM applications, enabling more reliable decision-making in sensitive domains by improving predictive distributions and uncertainty estimation.
LLMs can now be trained with a more nuanced understanding of uncertainty, moving beyond scalar predictions to generate calibrated probabilistic outputs, which impacts reliability and applicability.
- · AI researchers
- · LLM developers
- · Industries requiring high-fidelity predictive models
- · Applications demanding strong uncertainty quantification
- · LLM applications with poor uncertainty handling
- · Simplistic regression models
- · Those reliant on uncalibrated point estimates
Language models will provide more robust and trustworthy predictions, especially in high-stakes environments.
This improved reliability will accelerate the adoption of LLMs in fields like finance, healthcare, and scientific discovery where uncertainty is paramount.
Enhanced predictive distributions could lead to more sophisticated autonomous AI agents capable of nuanced risk assessment and decision-making.
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Read at arXiv cs.LG