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).
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.
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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.
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.
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...
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...
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...
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.
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.
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.
arXiv AI
Jun 2
One Bias After Another: Mechanistic Reward Shaping and Persistent Biases in Language Reward Models
arXiv:2603. 03291v2 Announce Type: replace-cross Abstract: Reward Models (RMs) are crucial for online alignment of language models (LMs) with human preferences.
arXiv AI
Sep 10
BaNEL: Exploration Posteriors for Generative Modeling Using Only Negative Rewards
arXiv:2510.09596v2 Announce Type: replace-cross Abstract: Today's generative models thrive with large amounts of supervised data and informative reward functions characterizing the quality of the gen...
arXiv AI
Jul 7
MARS: Margin and Semantic-Aware Data Augmentation for Reward Modeling
arXiv:2602. 17658v3 Announce Type: replace-cross Abstract: Reward modeling is central to alignment pipelines such as RLHF, RLAIF, and PPO-based policy optimization, yet its reliability is constrained by limited and heterogeneous human preference data that are expensive to collect at scale.