Adaptive Margin RLHF via Preference over Preferences
Fuente:
arXiv
Saved in:
| Main Authors: | Chittepu, Yaswanth, Singhal, Prasann, Durrett, Greg, Niekum, Scott |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Safe RLHF Beyond Expectation: Stochastic Dominance for Universal Spectral Risk Control
by: Chittepu, Yaswanth, et al.
Published: (2026)
by: Chittepu, Yaswanth, et al.
Published: (2026)
A Long Way to Go: Investigating Length Correlations in RLHF
by: Singhal, Prasann, et al.
Published: (2023)
by: Singhal, Prasann, et al.
Published: (2023)
Reinforcement Learning from Human Feedback with High-Confidence Safety Constraints
by: Chittepu, Yaswanth, et al.
Published: (2025)
by: Chittepu, Yaswanth, et al.
Published: (2025)
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms
by: Rafailov, Rafael, et al.
Published: (2024)
by: Rafailov, Rafael, et al.
Published: (2024)
D2PO: Discriminator-Guided DPO with Response Evaluation Models
by: Singhal, Prasann, et al.
Published: (2024)
by: Singhal, Prasann, et al.
Published: (2024)
To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
by: Sprague, Zayne, et al.
Published: (2024)
by: Sprague, Zayne, et al.
Published: (2024)
Active Preference Optimization for Sample Efficient RLHF
by: Das, Nirjhar, et al.
Published: (2024)
by: Das, Nirjhar, et al.
Published: (2024)
WPO: Enhancing RLHF with Weighted Preference Optimization
by: Zhou, Wenxuan, et al.
Published: (2024)
by: Zhou, Wenxuan, et al.
Published: (2024)
SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation
by: Divekar, Abhishek, et al.
Published: (2024)
by: Divekar, Abhishek, et al.
Published: (2024)
AlphaDPO: Adaptive Reward Margin for Direct Preference Optimization
by: Wu, Junkang, et al.
Published: (2024)
by: Wu, Junkang, et al.
Published: (2024)
MaxMin-RLHF: Alignment with Diverse Human Preferences
by: Chakraborty, Souradip, et al.
Published: (2024)
by: Chakraborty, Souradip, et al.
Published: (2024)
Towards Data-Centric RLHF: Simple Metrics for Preference Dataset Comparison
by: Shen, Judy Hanwen, et al.
Published: (2024)
by: Shen, Judy Hanwen, et al.
Published: (2024)
Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF
by: Xie, Tengyang, et al.
Published: (2024)
by: Xie, Tengyang, et al.
Published: (2024)
RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs
by: Dang, John, et al.
Published: (2024)
by: Dang, John, et al.
Published: (2024)
Best-of-Venom: Attacking RLHF by Injecting Poisoned Preference Data
by: Baumgärtner, Tim, et al.
Published: (2024)
by: Baumgärtner, Tim, et al.
Published: (2024)
Larger or Smaller Reward Margins to Select Preferences for Alignment?
by: Huang, Kexin, et al.
Published: (2025)
by: Huang, Kexin, et al.
Published: (2025)
PropMEND: Hypernetworks for Knowledge Propagation in LLMs
by: Liu, Zeyu Leo, et al.
Published: (2025)
by: Liu, Zeyu Leo, et al.
Published: (2025)
Contrastive Learning to Improve Retrieval for Real-world Fact Checking
by: Sriram, Aniruddh, et al.
Published: (2024)
by: Sriram, Aniruddh, et al.
Published: (2024)
On the Role of Preference Variance in Preference Optimization
by: Guo, Jiacheng, et al.
Published: (2025)
by: Guo, Jiacheng, et al.
Published: (2025)
Pareto-Optimal Learning from Preferences with Hidden Context
by: Bahlous-Boldi, Ryan, et al.
Published: (2024)
by: Bahlous-Boldi, Ryan, et al.
Published: (2024)
Preference Learning Algorithms Do Not Learn Preference Rankings
by: Chen, Angelica, et al.
Published: (2024)
by: Chen, Angelica, et al.
Published: (2024)
Geometric-Averaged Preference Optimization for Soft Preference Labels
by: Furuta, Hiroki, et al.
Published: (2024)
by: Furuta, Hiroki, et al.
Published: (2024)
HelpSteer2-Preference: Complementing Ratings with Preferences
by: Wang, Zhilin, et al.
Published: (2024)
by: Wang, Zhilin, et al.
Published: (2024)
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model
by: Wan, Xu, et al.
Published: (2025)
by: Wan, Xu, et al.
Published: (2025)
An Adaptive Placement and Parallelism Framework for Accelerating RLHF Training
by: Xiao, Youshao, et al.
Published: (2023)
by: Xiao, Youshao, et al.
Published: (2023)
Stable Preference Optimization: A Bilevel Approach to Catastrophic Preference Shift
by: Jian, Chengtao, et al.
Published: (2025)
by: Jian, Chengtao, et al.
Published: (2025)
AMPO: Active Multi-Preference Optimization for Self-play Preference Selection
by: Gupta, Taneesh, et al.
Published: (2025)
by: Gupta, Taneesh, et al.
Published: (2025)
Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment
by: Kim, Dongyoung, et al.
Published: (2024)
by: Kim, Dongyoung, et al.
Published: (2024)
Policy-labeled Preference Learning: Is Preference Enough for RLHF?
by: Cho, Taehyun, et al.
Published: (2025)
by: Cho, Taehyun, et al.
Published: (2025)
RLHF Workflow: From Reward Modeling to Online RLHF
by: Dong, Hanze, et al.
Published: (2024)
by: Dong, Hanze, et al.
Published: (2024)
ComPO: Preference Alignment via Comparison Oracles
by: Chen, Peter, et al.
Published: (2025)
by: Chen, Peter, et al.
Published: (2025)
ProofWala: A Framework for Multilingual Proof Data Synthesis and Theorem-Proving
by: Thakur, Amitayush, et al.
Published: (2025)
by: Thakur, Amitayush, et al.
Published: (2025)
Influencing Humans to Conform to Preference Models for RLHF
by: Hatgis-Kessell, Stephane, et al.
Published: (2025)
by: Hatgis-Kessell, Stephane, et al.
Published: (2025)
OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework
by: Hu, Jian, et al.
Published: (2024)
by: Hu, Jian, et al.
Published: (2024)
PerPO: Perceptual Preference Optimization via Discriminative Rewarding
by: Zhu, Zining, et al.
Published: (2025)
by: Zhu, Zining, et al.
Published: (2025)
Filtered Direct Preference Optimization
by: Morimura, Tetsuro, et al.
Published: (2024)
by: Morimura, Tetsuro, et al.
Published: (2024)
Direct Preference Optimization with an Offset
by: Amini, Afra, et al.
Published: (2024)
by: Amini, Afra, et al.
Published: (2024)
Post-edits Are Preferences Too
by: Berger, Nathaniel, et al.
Published: (2024)
by: Berger, Nathaniel, et al.
Published: (2024)
Self-Consistency Preference Optimization
by: Prasad, Archiki, et al.
Published: (2024)
by: Prasad, Archiki, et al.
Published: (2024)
DPO Kernels: A Semantically-Aware, Kernel-Enhanced, and Divergence-Rich Paradigm for Direct Preference Optimization
by: Das, Amitava, et al.
Published: (2025)
by: Das, Amitava, et al.
Published: (2025)
Similar Items
-
Safe RLHF Beyond Expectation: Stochastic Dominance for Universal Spectral Risk Control
by: Chittepu, Yaswanth, et al.
Published: (2026) -
A Long Way to Go: Investigating Length Correlations in RLHF
by: Singhal, Prasann, et al.
Published: (2023) -
Reinforcement Learning from Human Feedback with High-Confidence Safety Constraints
by: Chittepu, Yaswanth, et al.
Published: (2025) -
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms
by: Rafailov, Rafael, et al.
Published: (2024) -
D2PO: Discriminator-Guided DPO with Response Evaluation Models
by: Singhal, Prasann, et al.
Published: (2024)