arXiv:2601.22648v2 Announce Type: replace-cross Abstract: The key to building trustworthy large language models (LLMs) lies in endowing them with inherent uncertainty expression capabilities, thereby mitigating overconfident errors in high-stakes applications. However, existing RL paradigms such as GRPO often suffer from Advantage Bias due to binary decision spaces and static uncertainty rewards, inducing either excessive conservatism or overconfidence. To tackle this challenge, this paper unveils the root causes of reward hacking and overconfidence in current RL paradigms incorporating uncert
The increasing deployment of large language models in high-stakes applications necessitates robust solutions for managing their inherent uncertainties and mitigating overconfident errors, driving research in this direction.
Improving the trustworthiness and reliability of AI models, particularly LLMs, is crucial for widespread adoption and for preventing potentially catastrophic failures in critical systems.
New policy optimization paradigms will enable AI systems, especially LLMs, to explicitly express and manage their uncertainty, leading to more conservative and reliable decision-making in sensitive scenarios.
- · AI developers
- · High-stakes application sectors (e.g., healthcare, finance)
- · Regulatory bodies
- · Consumers of AI-driven services
- · Developers of overconfident, black-box AI models
- · Sectors reliant on unverified AI outputs
More secure and trustworthy deployment of large language models across various industries.
Increased public and institutional confidence in AI systems, accelerating adoption in regulated environments.
New ethical frameworks and regulatory standards emerging around uncertainty quantification and expression in AI, potentially globalizing best practices for safe AI development.
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