arXiv Machine Learning
Jun 8
Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models
arXiv:2505. 10892v2 Announce Type: replace Abstract: Post-training LLMs with RLHF and preference optimization methods (e.
Multi-Objective Exploration and Preference Optimization via Mutual Information
Aligning large language models with diverse and heterogeneous human values requires multi-objective alignment methods to effectively trade off conflicting preference dimensions. Current methods achieve this trade-off by training policies conditioned on preference vectors and leveraging online direct preference optimization.
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arXiv AI
Jul 7
Adaptive Margin RLHF via Preference over Preferences
arXiv:2509. 22851v4 Announce Type: replace-cross Abstract: Margin-based optimization is fundamental to improving generalization and robustness in classification tasks.
arXiv AI
Jun 4
Sparse Mixture-of-Experts Reward Models Learn Interpretable and Specialized Experts for Personalized Preference Modeling
arXiv:2606. 04284v1 Announce Type: cross Abstract: Preference modeling plays a central role in reinforcement learning from human feedback (RLHF), enabling large language models (LLMs) to align with human values.
arXiv AI
Jun 10
A Comprehensive Survey of Direct Preference Optimization: Datasets, Theories, Variants, and Applications
arXiv:2410. 15595v4 Announce Type: replace Abstract: With the rapid advancement of large language models (LLMs), aligning policy models with human preferences has become increasingly critical.
arXiv Machine Learning
Jun 30
REAR: Test-time Preference Realignment through Reward Decomposition
arXiv:2606. 30339v1 Announce Type: cross Abstract: Aligning large language models (LLMs) with diverse user preferences is a critical yet challenging task.
arXiv AI
Jul 24
Reliability-Aware LLM Alignment from Inconsistent Human Feedback
arXiv:2607. 20515v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is critical for aligning Large Language Models (LLMs) with human preferences.
arXiv AI
Jun 9
Provably Efficient Personalized Multi-Objective Bandits with Proactive Conversational Queries
arXiv:2606. 08410v1 Announce Type: cross Abstract: Personalized decision-making in multi-objective bandits requires learning user-specific trade-offs among competing objectives.
arXiv AI
Jul 7
Unbiased Alignment for Large Language Models with Noisy Preferences
arXiv:2607. 03248v1 Announce Type: cross Abstract: The alignment of large language models with human preferences is commonly achieved through Reinforcement Learning from Human Feedback or Direct Preference Optimization.
arXiv AI
Aug 3
LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback
arXiv:2607. 29559v1 Announce Type: new Abstract: Reinforcement Learning (RL) systems are typically trained using a single, well-specified scalar reward function.
arXiv AI
Jun 2
Simultaneous Multi-objective Alignment Across Verifiable and Non-verifiable Rewards
arXiv:2510. 01167v2 Announce Type: replace-cross Abstract: Aligning large language models to human preferences is inherently multidimensional, yet most pipelines collapse heterogeneous signals into a single objective.
arXiv AI
Aug 12
Multiplayer Nash Preference Optimization
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 AI
Jun 2
Value-Free Policy Optimization via Reward Partitioning
arXiv:2506. 13702v4 Announce Type: replace-cross Abstract: Single-trajectory preference optimization methods learn from datasets of ((prompt, response, reward)) tuples, offering a practical alternative to pairwise preference learning by directly leveraging scalar feedback.