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.

Summary generated by The Flow from the publisher's feed. 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 AI

Jun 19

Uncertainty-Aware Reward Modeling for Stable RLHF

arXiv:2606. 19818v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) aligns large language models by training reward models on preference data and optimizing policies to maximize predicted rewards.

By Licheng Pan, Haocheng Yang, Haoxuan Li, Yichen Sun, Yunsheng Lu, Shijian Wang, Lei Shen, Yuan Lu, Zhixuan Chu, Hao Wang

llms reinforcement-learning safety

arXiv Machine Learning

Jun 10

Gradient-Guided Reward Optimization for Inference-time Alignment

arXiv:2606. 09635v1 Announce Type: cross Abstract: Ensuring the reliability of Large Language Models (LLMs) under distribution drift requires inference-time adaptation.

By Hankun Lin, Ruqi Zhang

llms reinforcement-learning benchmarks safety

arXiv AI

Jul 20

Debiasing Text-to-Image Evaluation via Implicit Cultural Alignment Reward Modeling

arXiv:2607. 15740v1 Announce Type: cross Abstract: As Text-to-Image (T2I) systems rapidly advance, evaluating the cultural authenticity of synthesized content has become increasingly important for fair and trustworthy generative AI.

By Bo-An Chang, Yu-Chih Chen

llms diffusion nlp 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

Uncertainty-Aware Reward Modeling for Stable RLHF

Gradient-Guided Reward Optimization for Inference-time Alignment

Debiasing Text-to-Image Evaluation via Implicit Cultural Alignment Reward Modeling