:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Peng, Andi, Sun, Yuying, Shu, Tianmin, Abel, David
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computation and Language
Online Access:	https://arxiv.org/abs/2405.14769
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Generalizing Reward Modeling for Out-of-Distribution Preference Learning
by: Jia, Chen
Published: (2024)

Preference Poisoning Attacks on Reward Model Learning
by: Wu, Junlin, et al.
Published: (2024)

Bayesian Preference Learning for Test-Time Steerable Reward Models
by: Hong, Jiwoo, et al.
Published: (2026)

DreamReward: Text-to-3D Generation with Human Preference
by: Ye, Junliang, et al.
Published: (2024)

Sentence-level Reward Model can Generalize Better for Aligning LLM from Human Preference
by: Qiu, Wenjie, et al.
Published: (2025)

Group Robust Preference Optimization in Reward-free RLHF
by: Ramesh, Shyam Sundhar, et al.
Published: (2024)

Robust Preference Optimization through Reward Model Distillation
by: Fisch, Adam, et al.
Published: (2024)

Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment
by: Sun, Shengyang, et al.
Published: (2025)

Learning How Hard to Think: Input-Adaptive Allocation of LM Computation
by: Damani, Mehul, et al.
Published: (2024)

PerPO: Perceptual Preference Optimization via Discriminative Rewarding
by: Zhu, Zining, et al.
Published: (2025)

Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards
by: Wang, Haoxiang, et al.
Published: (2024)

Beyond Scalar Reward Model: Learning Generative Judge from Preference Data
by: Ye, Ziyi, et al.
Published: (2024)

C2: Scalable Rubric-Augmented Reward Modeling from Binary Preferences
by: Kawabata, Akira, et al.
Published: (2026)

SimPO: Simple Preference Optimization with a Reference-Free Reward
by: Meng, Yu, et al.
Published: (2024)

Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts
by: Wang, Haoxiang, et al.
Published: (2024)

T-REG: Preference Optimization with Token-Level Reward Regularization
by: Zhou, Wenxuan, et al.
Published: (2024)

Larger or Smaller Reward Margins to Select Preferences for Alignment?
by: Huang, Kexin, et al.
Published: (2025)

Preference Learning Algorithms Do Not Learn Preference Rankings
by: Chen, Angelica, et al.
Published: (2024)

Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy
by: Liu, Chris Yuhao, et al.
Published: (2025)

COPR: Continual Learning Human Preference through Optimal Policy Regularization
by: Zhang, Han, et al.
Published: (2023)

Beyond Preferences: Learning Alignment Principles Grounded in Human Reasons and Values
by: Bell, Henry, et al.
Published: (2026)

On the Limited Generalization Capability of the Implicit Reward Model Induced by Direct Preference Optimization
by: Lin, Yong, et al.
Published: (2024)

General Preference Reinforcement Learning
by: Umer, Muhammad, et al.
Published: (2026)

West-of-N: Synthetic Preferences for Self-Improving Reward Models
by: Pace, Alizée, et al.
Published: (2024)

AlphaDPO: Adaptive Reward Margin for Direct Preference Optimization
by: Wu, Junkang, et al.
Published: (2024)

Simultaneous Reward Distillation and Preference Learning: Get You a Language Model Who Can Do Both
by: Nath, Abhijnan, et al.
Published: (2024)

DreamDPO: Aligning Text-to-3D Generation with Human Preferences via Direct Preference Optimization
by: Zhou, Zhenglin, et al.
Published: (2025)

Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning
by: Poddar, Sriyash, et al.
Published: (2024)

Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences
by: Karlekar, Sweta, et al.
Published: (2026)

Selective Preference Optimization via Token-Level Reward Function Estimation
by: Yang, Kailai, et al.
Published: (2024)

Difficulty-Based Preference Data Selection by DPO Implicit Reward Gap
by: Qi, Xuan, et al.
Published: (2025)

Whose Preferences? Differences in Fairness Preferences and Their Impact on the Fairness of AI Utilizing Human Feedback
by: Lerner, Emilia Agis, et al.
Published: (2024)

AutoRule: Reasoning Chain-of-thought Extracted Rule-based Rewards Improve Preference Learning
by: Wang, Tevin, et al.
Published: (2025)

When In-Distribution Gains Fail: Evaluating Weak-to-Strong Reward Models under Preference Shift
by: Le, Khoi, et al.
Published: (2026)

How Many Human Judgments Are Enough? Feasibility Limits of Human Preference Evaluation
by: Lee, Wilson Y.
Published: (2026)

Active Preference Learning for Large Language Models
by: Muldrew, William, et al.
Published: (2024)

On the Role of Preference Variance in Preference Optimization
by: Guo, Jiacheng, et al.
Published: (2025)

Approximating Human Preferences Using a Multi-Judge Learned System
by: Sprejer, Eitán, et al.
Published: (2025)

Personalized Adaptation via In-Context Preference Learning
by: Lau, Allison, et al.
Published: (2024)

HelpSteer3-Preference: Open Human-Annotated Preference Data across Diverse Tasks and Languages
by: Wang, Zhilin, et al.
Published: (2025)