{"total":36,"rows":[{"id":"2503.11701","title":"A Survey of Direct Preference Optimization","created":"2025-03-12","cited_by_count":36,"area":"alignment"},{"id":"2502.00883","title":"SimPER: A Minimalist Approach to Preference Alignment without Hyperparameters","created":"2025-02-02","cited_by_count":25,"area":"alignment"},{"id":"2503.05079","title":"On a Connection Between Imitation Learning and RLHF","created":"2025-03-07","cited_by_count":13,"area":"alignment"},{"id":"2504.03784","title":"Robust Reinforcement Learning from Human Feedback for Large Language Models Fine-Tuning","created":"2025-04-03","cited_by_count":13,"area":"alignment"},{"id":"2505.05465","title":"ComPO: Preference Alignment via Comparison Oracles","created":"2025-05-08","cited_by_count":13,"area":"alignment"},{"id":"2503.01864","title":"Larger or Smaller Reward Margins to Select Preferences for Alignment?","created":"2025-02-25","cited_by_count":11,"area":"alignment"},{"id":"2505.20081","title":"Inference-time Alignment in Continuous Space","created":"2025-05-26","cited_by_count":7,"area":"alignment"},{"id":"2603.00025","title":"TAB-PO: Preference Optimization with a Token-Level Adaptive Barrier for Token-Critical Structured Generation","created":"2026-02-03","cited_by_count":7,"area":"alignment"},{"id":"2506.07165","title":"AMoPO: Adaptive Multi-objective Preference Optimization without Reward Models and Reference Models","created":"2025-06-08","cited_by_count":6,"area":"alignment"},{"id":"2505.23316","title":"Proximalized Preference Optimization for Diverse Feedback Types: A Decomposed Perspective on DPO","created":"2025-05-29","cited_by_count":5,"area":"alignment"},{"id":"2601.23096","title":"CATTO: Balancing Preferences and Confidence in Language Models","created":"2026-01-30","cited_by_count":4,"area":"alignment"},{"id":"2502.01237","title":"The Differences Between Direct Alignment Algorithms are a Blur","created":"2025-02-03","cited_by_count":3,"area":"alignment"},{"id":"2505.12435","title":"SGDPO: Self-Guided Direct Preference Optimization for Language Model Alignment","created":"2025-05-18","cited_by_count":3,"area":"alignment"},{"id":"2506.01523","title":"Beyond RLHF: A Unified Theoretical Framework of Alignment","created":"2025-06-02","cited_by_count":3,"area":"alignment"},{"id":"2511.07691","title":"CAPO: Confidence Aware Preference Optimization Learning for Multilingual Preferences","created":"2025-11-10","cited_by_count":3,"area":"alignment"},{"id":"2502.17507","title":"C2-DPO: Constrained Controlled Direct Preference Optimization","created":"2025-02-22","cited_by_count":2,"area":"alignment"},{"id":"2504.06659","title":"Leveraging Machine Unlearning for Cost-Efficient Preference Alignment","created":"2025-04-09","cited_by_count":2,"area":"alignment"},{"id":"2505.06273","title":"Policy-labeled Preference Learning: Is Preference Enough for RLHF?","created":"2025-05-06","cited_by_count":2,"area":"alignment"},{"id":"2511.09385","title":"AMaPO: Adaptive Margin-attached Preference Optimization for Language Model Alignment","created":"2025-11-12","cited_by_count":2,"area":"alignment"},{"id":"2602.00603","title":"Direct Preference Optimization with Rating Information: Practical Algorithms and Provable Gains","created":"2026-01-31","cited_by_count":2,"area":"alignment"},{"id":"2602.00954","title":"Small-Margin Preferences Still Matter-If You Train Them Right","created":"2026-02-01","cited_by_count":2,"area":"alignment"},{"id":"2505.23761","title":"Differential Information Distribution: A Bayesian Perspective on Direct Preference Optimization","created":"2025-05-29","cited_by_count":1,"area":"alignment"},{"id":"2510.22881","title":"Offline Preference Optimization via Maximum Marginal Likelihood Estimation","created":"2025-10-27","cited_by_count":1,"area":"alignment"},{"id":"2602.09533","title":"Autoregressive Direct Preference Optimization","created":"2026-02-10","cited_by_count":1,"area":"alignment"},{"id":"2603.07211","title":"CompassDPO: Dynamics-Controlled Direct Preference Optimization for Robust Safety Alignment","created":"2026-03-07","cited_by_count":1,"area":"alignment"},{"id":"2603.18258","title":"Sharpness-Aware Minimization in Logit Space Efficiently Enhances Direct Preference Optimization","created":"2026-03-18","cited_by_count":1,"area":"alignment"},{"id":"2605.02626","title":"Gradient-Gated DPO: Stabilizing Preference Optimization in Language Models","created":"2026-05-04","cited_by_count":1,"area":"alignment"},{"id":"2605.28440","title":"AdaDPO: Self-Adaptive Direct Preference Optimization with Balanced Gradient Updates","created":"2026-05-27","cited_by_count":1,"area":"alignment"},{"id":"2506.03557","title":"BPO: Revisiting Preference Modeling in Direct Preference Optimization","created":"2025-06-04","cited_by_count":0,"area":"alignment"},{"id":"2506.08998","title":"On Monotonicity in AI Alignment","created":"2025-06-10","cited_by_count":0,"area":"alignment"},{"id":"2507.20133","title":"Sem-DPO: Mitigating Semantic Inconsistency in Preference Optimization for Prompt Engineering","created":"2025-07-27","cited_by_count":0,"area":"alignment"},{"id":"2602.06788","title":"Displacement-Resistant Extensions of DPO with Nonconvex $f$-Divergences","created":"2026-02-06","cited_by_count":0,"area":"alignment"},{"id":"2605.20696","title":"Distributed Direct Preference Optimization","created":"2026-05-20","cited_by_count":0,"area":"alignment"},{"id":"2606.16074","title":"PVminerLLM2: Improving Structured Extraction of Patient Voice via Preference Optimization","created":"2026-06-15","cited_by_count":0,"area":"alignment"},{"id":"2609.19144","title":"A Zeroth-Order Paradigm for LLM Preference Alignment","created":"2026-09-16","cited_by_count":0,"area":"alignment"},{"id":"2610.12885","title":"Personalized Federated Direct Preference Optimization via LoRA Adapters","created":"2026-10-09","cited_by_count":0,"area":"alignment"}],"next_cursor":null}