arXiv AI June 2, 2026 By Zhibin Duan, Guowei Rong, Zhuo Li, Bo Chen, Mingyuan Zhou, Dandan Guo

Mitigating Reward Hacking in RLHF via Bayesian Non-negative Reward Modeling

arXiv:2602. 10623v2 Announce Type: replace-cross Abstract: Reward models learned from human preferences are central to aligning large language models (LLMs) via reinforcement learning from human feedback, yet they are often vulnerable to reward hacking due to noisy annotations and systematic biases such as response length or style.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 5

Enhancing VLM Reward Models Through Structure-Aware Fine-Tuning

arXiv:2608. 03875v1 Announce Type: cross Abstract: Designing effective reward functions remains a major bottleneck in Reinforcement Learning (RL).

By Pyrros Koussios, Chenhao Li, Xin Chen, Andreas Krause

llms diffusion reinforcement-learning fine-tuning multimodal safety

arXiv AI

Jun 16

Latent Thought Flow: Efficient Latent Reasoning in Large Language Models

arXiv:2606. 16222v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly rely on intermediate reasoning, yet explicit Chain-of-Thought (CoT) suffers from a linguistic space bottleneck: each thought must be decoded into tokens, causing high inference overhead.

By Xiandong Zou, Jing Huang, Jianshu Li, Pan Zhou

llms fine-tuning

arXiv AI

Jun 10

When Distance Distracts: Representation Distance Bias in BT-Loss for Reward Models

arXiv:2512. 06343v3 Announce Type: replace-cross Abstract: Reward models are central to Large Language Model (LLM) alignment within the framework of RLHF.

By Tong Xie, Andrew Bai, Yuanhao Ban, Yunqi Hong, Haoyu Li, Cho-Jui Hsieh

llms reinforcement-learning safety

arXiv Machine Learning

2d ago

Preference Instability in Reward Models: Detection and Mitigation via Sparse Autoencoders

arXiv:2605.16339v2 Announce Type: replace Abstract: Preference learning in large language models relies on reward models as proxies for human judgment. However, these models frequently exhibit prefer...

By Shunchang Liu, Xin Chen, Belen Martin Urcelay, Francesco Croce

llms diffusion reinforcement-learning benchmarks safety

arXiv Machine Learning

Sep 1

Amortizing intractable inference in large language models

arXiv:2310.04363v3 Announce Type: replace Abstract: Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This limits t...

By Edward J. Hu, Moksh Jain, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, Esmeralda S. Whitammer

llms agents reinforcement-learning fine-tuning

arXiv AI

4d ago

Diffusion Reward Models

arXiv:2609.33803v2 Announce Type: replace-cross Abstract: Reward models underpin the alignment of large language models, yet the dominant designs reduce each prompt--response pair to a point estimate...

By Xiangyang Wang, Bingxiang He, Zeyuan Liu, Jiaze Wang, Ziqing Qiao, Yuxin Zuo, Huan-ang Gao, Cheng Qian, Wenbin Zhang, Ran Li, Youbang Sun, Ning Ding, Yuanchun Shi, Zhiyuan Liu, Chaojun Xiao, Chun Yu

llms diffusion reinforcement-learning multimodal benchmarks safety

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Enhancing VLM Reward Models Through Structure-Aware Fine-Tuning

Latent Thought Flow: Efficient Latent Reasoning in Large Language Models

When Distance Distracts: Representation Distance Bias in BT-Loss for Reward Models

Preference Instability in Reward Models: Detection and Mitigation via Sparse Autoencoders

Amortizing intractable inference in large language models

Diffusion Reward Models