Why Semantic Entropy Fails: Geometry-Aware and Calibrated Uncertainty for Policy Optimization
arXiv:2605.21801v1 Announce Type: new Abstract: Post-training has become central to improving reasoning and alignment in large language models, where critic-free models enable scalable learning from model-generated outputs but lack principled mechanisms to distinguish informative from noisy signals. Recent approaches leverage response-level measures as uncertainty signals to regulate group-based optimization methods such as GRPO. Yet their empirical success remains unstable and unclear in how they influence optimization dynamics. In this paper, we provide, to our knowledge, the first principle
The increasing reliance on large language models and the push for autonomous AI operations necessitates more robust uncertainty quantification to manage risks and improve reliability.
This research directly addresses a critical limitation in current AI approaches, where distinguishing reliable signals from noise in model-generated outputs is essential for effective optimization and deployment.
A principled method for geometry-aware and calibrated uncertainty could significantly improve the stability and performance of post-training methods for large language models, leading to more trustworthy AI systems.
- · AI developers
- · Companies deploying autonomous AI
- · Researchers in AI safety and alignment
- · Developers relying solely on ad-hoc uncertainty measures
- · Systems with high reliance on noisy model outputs
Improved post-training stability and performance for large language models, reducing errors and increasing efficiency.
Faster and more reliable development of AI agents capable of complex decision-making in real-world scenarios due to enhanced trustworthiness.
Accelerated adoption of AI across sensitive sectors where high calibration and uncertainty awareness are paramount, decentralizing advanced AI capabilities.
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Read at arXiv cs.LG