The paper reinterprets AI alignment as a social choice problem, framing it as linear optimization over a convex impact space. This approach links alignment protocols to welfare outcomes, enabling the use of welfare economics and mechanism design tools. The authors demonstrate strategyproof, unanimous mechanisms like voting-by-issues and random-dictatorship, and derive alignment protocols that maximize utilitarian welfare while respecting harm constraints, validated on real human preference data.
arXiv:2403.05006v2 Announce Type: replace-cross Abstract: Pluralistic alignment requires learning from feedback that reflects persistent and potentially conflicting stakeholder preferences while ulti...
arXiv:2509. 23102v4 Announce Type: replace Abstract: Reinforcement learning from human feedback (RLHF) has emerged as the standard paradigm for aligning large language models with human preferences.
arXiv:2605. 01642v2 Announce Type: replace Abstract: Prevailing alignment methods target a fixed set of preferences and therefore risk forcing value lock-in as societal norms evolve over time.
arXiv:2512. 15765v3 Announce Type: replace Abstract: Data valuation is a natural framework for understanding which preference datasets matter most when aligning a Large Language Model (LLM) using multiple sources.
arXiv:2607. 00001v1 Announce Type: new Abstract: Most approaches to AI alignment treat human preferences as fixed targets to be inferred and optimized.
arXiv:2505. 10892v2 Announce Type: replace Abstract: Post-training LLMs with RLHF and preference optimization methods (e.
The article surveys AI alignment from a game-theoretic perspective, focusing on how large language models and AI agents can be aligned with complex human values in high-risk settings. It categorizes recent progress around key game-theoretic elements and addresses three main challenges: preference diversity, alignment priority, and temporal dynamics. The survey clarifies where game theory benefits current alignment methods, where its application is looser, and what remains to be tackled for robust, adaptive, and verifiable AI systems.
The paper introduces CurriPO, a tree‑structured curriculum that automatically adapts to diverse user reward models in AI alignment tasks. By exploiting the natural hierarchy between easy‑ and hard‑to‑optimize reward models, CurriPO covers a broad user population in a single traversal, reusing previously incorporated reward models. Experiments on personalized continuous control show that CurriPO improves population satisfaction by 1.2–2.1× over the strongest baseline while cutting training time and better serving users traditionally underserved by conventional optimization.
The paper studies how to safely delegate action approval to multiple AI reviewers when the reviewers themselves may be misaligned. It introduces a weaker condition—k‑robust coalitional alignment—under which a threshold rule that tolerates up to k disapprovals guarantees that the principal’s expected utility is at least as good as a baseline policy. The authors extend this characterization to sequential decision‑making in discounted MDPs and show that full‑panel coverage of reward functions ensures safety in Nash equilibria, while more permissive thresholds can lead to unsafe outcomes. Experiments demonstrate that collective review can remain sound even when individual reviewers are not fully aligned, provided some disapprovals are allowed.
arXiv:2608. 12302v1 Announce Type: new Abstract: We present a formal process to enable non-experts to instantiate and iterate on human-aligned reward functions, i.
arXiv:2607. 28881v1 Announce Type: new Abstract: As more responsibility is placed upon AI systems, it becomes increasingly important to guarantee that these systems are aligned with humanity.
The paper introduces CurriPO, a tree‑structured curriculum that adapts to diverse user reward models in AI alignment. By automatically building a curriculum that branches and reuses reward models, it addresses the problem of users whose reward models are hard to optimize, a group often underserved by conventional methods. Experiments on personalized continuous control demonstrate that CurriPO improves population satisfaction by 1.2–2.1× over the best baseline while cutting training time.