评分 5.0 · 来源：cs.AI updates on arXiv.org · 发布于 2026-04-14
评分依据：中等质量：常规学术论文，有适度参考价值
Tail-Aware Information-Theoretic Generalization for RLHF and SGLD
arXiv:2604.10727v1 Announce Type: cross Abstract: Classical information-theoretic generalization bounds typically control the generalization gap through KL-based mutual information and therefore rely on boundedness or sub-Gaussian tails via the moment generating function (MGF). In many modern pipelines, such as robust learning, RLHF, and stochastic optimization, losses and rewards can be heavy-tailed, and MGFs may not exist, rendering KL-based tools ineffective. We develop a tail-dependent…