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arXiv:2610.28229v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) minimizes empirical risk over observed preference comparisons, implicitly assuming that the training preference...
arXiv:2605. 00155v3 Announce Type: replace Abstract: Reinforcement learning from human feedback (RLHF) is a central post-training tool for aligning large language models, but its training reward is only a learned proxy for true human utility.
arXiv:2505. 10892v2 Announce Type: replace Abstract: Post-training LLMs with RLHF and preference optimization methods (e.
arXiv:2504. 10796v4 Announce Type: replace-cross Abstract: Distributionally robust optimization (DRO) is widely used for decision-making under uncertainty, but its adversarial focus on worst-case loss can lead to overly conservative policies.
The paper introduces Diagnostic-Transport DRO (DT‑DRO), a method that calibrates the geometry of the ambiguity set in distributionally robust optimization using held‑out data. By applying the conditional probability integral transform to detect systematic probability misallocation, DT‑DRO adjusts both the ambiguity‑set center and ground cost through an outcome‑level transport, yielding a computationally tractable dual reformulation. The authors provide theoretical guarantees on ambiguity radii and decision‑risk bounds that improve as estimation errors shrink, and demonstrate that DT‑DRO can remove the robustness floor caused by model misspecification in synthetic and power‑outage experiments.
arXiv:2608. 13133v1 Announce Type: cross Abstract: Distributional shifts arise when the target deployment environment differs from the source environment that generated the training data.