WARM: On the Benefits of Weight Averaged Reward Models
Fuente:
arXiv
Saved in:
| Main Authors: | Ramé, Alexandre, Vieillard, Nino, Hussenot, Léonard, Dadashi, Robert, Cideron, Geoffrey, Bachem, Olivier, Ferret, Johan |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
WARP: On the Benefits of Weight Averaged Rewarded Policies
by: Ramé, Alexandre, et al.
Published: (2024)
by: Ramé, Alexandre, et al.
Published: (2024)
BOND: Aligning LLMs with Best-of-N Distillation
by: Sessa, Pier Giuseppe, et al.
Published: (2024)
by: Sessa, Pier Giuseppe, et al.
Published: (2024)
On Teacher Hacking in Language Model Distillation
by: Tiapkin, Daniil, et al.
Published: (2025)
by: Tiapkin, Daniil, et al.
Published: (2025)
Diversity-Rewarded CFG Distillation
by: Cideron, Geoffrey, et al.
Published: (2024)
by: Cideron, Geoffrey, et al.
Published: (2024)
Conditional Language Policy: A General Framework for Steerable Multi-Objective Finetuning
by: Wang, Kaiwen, et al.
Published: (2024)
by: Wang, Kaiwen, et al.
Published: (2024)
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes
by: Agarwal, Rishabh, et al.
Published: (2023)
by: Agarwal, Rishabh, et al.
Published: (2023)
Imitating Language via Scalable Inverse Reinforcement Learning
by: Wulfmeier, Markus, et al.
Published: (2024)
by: Wulfmeier, Markus, et al.
Published: (2024)
Extrapolative Weight Averaging Reveals Correctness-Efficiency Frontiers in Code RL
by: Zheng, Kunhao, et al.
Published: (2026)
by: Zheng, Kunhao, et al.
Published: (2026)
Reward Model Overoptimisation in Iterated RLHF
by: Wolf, Lorenz, et al.
Published: (2025)
by: Wolf, Lorenz, et al.
Published: (2025)
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
by: Botev, Aleksandar, et al.
Published: (2024)
by: Botev, Aleksandar, et al.
Published: (2024)
TIAM -- A Metric for Evaluating Alignment in Text-to-Image Generation
by: Grimal, Paul, et al.
Published: (2023)
by: Grimal, Paul, et al.
Published: (2023)
Towards Large Language Models that Benefit for All: Benchmarking Group Fairness in Reward Models
by: Song, Kefan, et al.
Published: (2025)
by: Song, Kefan, et al.
Published: (2025)
RLHF in an SFT Way: From Optimal Solution to Reward-Weighted Alignment
by: Du, Yuhao, et al.
Published: (2025)
by: Du, Yuhao, et al.
Published: (2025)
RewardAnything: Generalizable Principle-Following Reward Models
by: Yu, Zhuohao, et al.
Published: (2025)
by: Yu, Zhuohao, et al.
Published: (2025)
RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
by: Lee, Harrison, et al.
Published: (2023)
by: Lee, Harrison, et al.
Published: (2023)
M-RewardBench: Evaluating Reward Models in Multilingual Settings
by: Gureja, Srishti, et al.
Published: (2024)
by: Gureja, Srishti, et al.
Published: (2024)
Rethinking Reward Model Evaluation Through the Lens of Reward Overoptimization
by: Kim, Sunghwan, et al.
Published: (2025)
by: Kim, Sunghwan, et al.
Published: (2025)
RewardUQ: A Unified Framework for Uncertainty-Aware Reward Models
by: Yang, Daniel, et al.
Published: (2026)
by: Yang, Daniel, et al.
Published: (2026)
Theoretical Benefit and Limitation of Diffusion Language Model
by: Feng, Guhao, et al.
Published: (2025)
by: Feng, Guhao, et al.
Published: (2025)
Process Reward Models That Think
by: Khalifa, Muhammad, et al.
Published: (2025)
by: Khalifa, Muhammad, et al.
Published: (2025)
On the Robustness of Reward Models for Language Model Alignment
by: Hong, Jiwoo, et al.
Published: (2025)
by: Hong, Jiwoo, et al.
Published: (2025)
How to Evaluate Reward Models for RLHF
by: Frick, Evan, et al.
Published: (2024)
by: Frick, Evan, et al.
Published: (2024)
Reward Models Identify Consistency, Not Causality
by: Xu, Yuhui, et al.
Published: (2025)
by: Xu, Yuhui, et al.
Published: (2025)
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning
by: Pignatelli, Eduardo, et al.
Published: (2023)
by: Pignatelli, Eduardo, et al.
Published: (2023)
Revisiting Weight Averaging for Model Merging
by: Choi, Jiho, et al.
Published: (2024)
by: Choi, Jiho, et al.
Published: (2024)
RM-R1: Reward Modeling as Reasoning
by: Chen, Xiusi, et al.
Published: (2025)
by: Chen, Xiusi, et al.
Published: (2025)
Quantile Regression for Distributional Reward Models in RLHF
by: Dorka, Nicolai
Published: (2024)
by: Dorka, Nicolai
Published: (2024)
Inference-Time Scaling for Generalist Reward Modeling
by: Liu, Zijun, et al.
Published: (2025)
by: Liu, Zijun, et al.
Published: (2025)
SALMON: Self-Alignment with Instructable Reward Models
by: Sun, Zhiqing, et al.
Published: (2023)
by: Sun, Zhiqing, et al.
Published: (2023)
Preference Poisoning Attacks on Reward Model Learning
by: Wu, Junlin, et al.
Published: (2024)
by: Wu, Junlin, et al.
Published: (2024)
Reward Modeling with Ordinal Feedback: Wisdom of the Crowd
by: Liu, Shang, et al.
Published: (2024)
by: Liu, Shang, et al.
Published: (2024)
Evaluating Robustness of Reward Models for Mathematical Reasoning
by: Kim, Sunghwan, et al.
Published: (2024)
by: Kim, Sunghwan, et al.
Published: (2024)
RewardFlow: Topology-Aware Reward Propagation on State Graphs for Agentic RL with Large Language Models
by: Feng, Xiao, et al.
Published: (2026)
by: Feng, Xiao, et al.
Published: (2026)
Reward Shaping to Mitigate Reward Hacking in RLHF
by: Fu, Jiayi, et al.
Published: (2025)
by: Fu, Jiayi, et al.
Published: (2025)
RLBFF: Binary Flexible Feedback to bridge between Human Feedback & Verifiable Rewards
by: Wang, Zhilin, et al.
Published: (2025)
by: Wang, Zhilin, et al.
Published: (2025)
Self-Generated Critiques Boost Reward Modeling for Language Models
by: Yu, Yue, et al.
Published: (2024)
by: Yu, Yue, et al.
Published: (2024)
Fine-tuning Language Models with Generative Adversarial Reward Modelling
by: Yu, Zhang Ze, et al.
Published: (2023)
by: Yu, Zhang Ze, et al.
Published: (2023)
Text2Reward: Reward Shaping with Language Models for Reinforcement Learning
by: Xie, Tianbao, et al.
Published: (2023)
by: Xie, Tianbao, et al.
Published: (2023)
R3: Robust Rubric-Agnostic Reward Models
by: Anugraha, David, et al.
Published: (2025)
by: Anugraha, David, et al.
Published: (2025)
Fine-Tuning Language Models with Reward Learning on Policy
by: Lang, Hao, et al.
Published: (2024)
by: Lang, Hao, et al.
Published: (2024)
Similar Items
-
WARP: On the Benefits of Weight Averaged Rewarded Policies
by: Ramé, Alexandre, et al.
Published: (2024) -
BOND: Aligning LLMs with Best-of-N Distillation
by: Sessa, Pier Giuseppe, et al.
Published: (2024) -
On Teacher Hacking in Language Model Distillation
by: Tiapkin, Daniil, et al.
Published: (2025) -
Diversity-Rewarded CFG Distillation
by: Cideron, Geoffrey, et al.
Published: (2024) -
Conditional Language Policy: A General Framework for Steerable Multi-Objective Finetuning
by: Wang, Kaiwen, et al.
Published: (2024)