Offline Constrained RLHF with Multiple Preference Oracles

arXiv:2604.00200 · cs.LG · Submitted 2026-03-31 · Read on arXiv

cs.LG

Submitted: 2026-03-31

Updated: 2026-09-18

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reinforcement Learning — 23× in this paper · explained in 4 episodes
• Alignment — 14× in this paper · explained in 1 episode
• Regularization — 8× in this paper · explained in 3 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Reward Function — 5× in this paper · explained in 2 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Pairwise Comparisons — 4× in this paper · explained in 2 episodes
• Regret — 3× in this paper · explained in 3 episodes
• Theoretical Guarantees — 3× in this paper · explained in 2 episodes
• AI Alignment — 2× in this paper · explained in 2 episodes
• Axioms — 2× in this paper · explained in 2 episodes
• Unified Framework — 2× in this paper · explained in 5 episodes
• Acquisition Function — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Differentially Private — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Minimax Regret — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Exploration-Exploitation in Constrained MDPs

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