:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Krishna, Shambhavi, Sahoo, Aishwarya
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computation and Language
Online Access:	https://arxiv.org/abs/2412.10529
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

RLHF Workflow: From Reward Modeling to Online RLHF
by: Dong, Hanze, et al.
Published: (2024)

Why Is RLHF Alignment Shallow? A Gradient Analysis
by: Young, Robin
Published: (2026)

Learning from Failures: Understanding LLM Alignment through Failure-Aware Inverse RL
by: Patel, Nyal, et al.
Published: (2025)

Latent Traits and Cross-Task Transfer: Deconstructing Dataset Interactions in LLM Fine-tuning
by: Krishna, Shambhavi, et al.
Published: (2025)

MaxMin-RLHF: Alignment with Diverse Human Preferences
by: Chakraborty, Souradip, et al.
Published: (2024)

Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy
by: Zhu, Yu, et al.
Published: (2024)

RLHF in an SFT Way: From Optimal Solution to Reward-Weighted Alignment
by: Du, Yuhao, et al.
Published: (2025)

Active Preference Optimization for Sample Efficient RLHF
by: Das, Nirjhar, et al.
Published: (2024)

RLHF: A comprehensive Survey for Cultural, Multimodal and Low Latency Alignment Methods
by: Sharma, Raghav, et al.
Published: (2025)

Failure Modes of Maximum Entropy RLHF
by: Çağatan, Ömer Veysel, et al.
Published: (2025)

OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework
by: Hu, Jian, et al.
Published: (2024)

FCoReBench: Can Large Language Models Solve Challenging First-Order Combinatorial Reasoning Problems?
by: Mittal, Chinmay, et al.
Published: (2024)

Evaluating Defences against Unsafe Feedback in RLHF
by: Rosati, Domenic, et al.
Published: (2024)

Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF
by: Gao, Zhaolin, et al.
Published: (2024)

Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions
by: Patel, Dhruvesh, et al.
Published: (2025)

Reward-Robust RLHF in LLMs
by: Yan, Yuzi, et al.
Published: (2024)

RLHF and IIA: Perverse Incentives
by: Xu, Wanqiao, et al.
Published: (2023)

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

Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models
by: Noukhovitch, Michael, et al.
Published: (2024)

Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF
by: Xie, Tengyang, et al.
Published: (2024)

Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization
by: Du, Yihan, et al.
Published: (2024)

Leveraging Domain Knowledge for Efficient Reward Modelling in RLHF: A Case-Study in E-Commerce Opinion Summarization
by: Nath, Swaroop, et al.
Published: (2024)

A Long Way to Go: Investigating Length Correlations in RLHF
by: Singhal, Prasann, et al.
Published: (2023)

How to Evaluate Reward Models for RLHF
by: Frick, Evan, et al.
Published: (2024)

Dataset Reset Policy Optimization for RLHF
by: Chang, Jonathan D., et al.
Published: (2024)

Measuring memorization in RLHF for code completion
by: Pappu, Aneesh, et al.
Published: (2024)

Reward Model Overoptimisation in Iterated RLHF
by: Wolf, Lorenz, et al.
Published: (2025)

Disentangled Safety Adapters Enable Efficient Guardrails and Flexible Inference-Time Alignment
by: Krishna, Kundan, et al.
Published: (2025)

On Importance of Pruning and Distillation for Efficient Low Resource NLP
by: Mirashi, Aishwarya, et al.
Published: (2024)

Does RLHF Scale? Exploring the Impacts From Data, Model, and Method
by: Hou, Zhenyu, et al.
Published: (2024)

Understanding the Performance Gap in Preference Learning: A Dichotomy of RLHF and DPO
by: Shi, Ruizhe, et al.
Published: (2025)

Quantile Regression for Distributional Reward Models in RLHF
by: Dorka, Nicolai
Published: (2024)

The Perfect Blend: Redefining RLHF with Mixture of Judges
by: Xu, Tengyu, et al.
Published: (2024)

ODIN: Disentangled Reward Mitigates Hacking in RLHF
by: Chen, Lichang, et al.
Published: (2024)

WPO: Enhancing RLHF with Weighted Preference Optimization
by: Zhou, Wenxuan, et al.
Published: (2024)

Understanding the Effects of RLHF on LLM Generalisation and Diversity
by: Kirk, Robert, et al.
Published: (2023)

RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation
by: Liang, Kaiqu, et al.
Published: (2025)

Information-Theoretic Reward Decomposition for Generalizable RLHF
by: Mao, Liyuan, et al.
Published: (2025)

General Exploratory Bonus for Optimistic Exploration in RLHF
by: Li, Wendi, et al.
Published: (2025)

Reward Shaping to Mitigate Reward Hacking in RLHF
by: Fu, Jiayi, et al.
Published: (2025)