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Yongyu Mu

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5 papers

2 author rows

AAAI Conference 2026 Conference Paper

GRAM-R²: Self-Training Generative Foundation Reward Models for Reward Reasoning

Chenglong Wang
Yongyu Mu
Hang Zhou
Yifu Huo
Ziming Zhu
Jiali Zeng
Murun Yang
Bei Li

Major progress in reward modeling over recent years has been driven by a paradigm shift from task-specific designs to generalist reward models. Despite this trend, developing effective reward models remains a fundamental challenge: the heavy reliance on large-scale labeled preference data. Pre-training on abundant unlabeled data offers a promising direction, but existing approaches fall short in instilling explicit reasoning capabilities into reward models. To bridge this gap, we propose a self-training approach that can leverage unlabeled data to scale up reward reasoning in reward models. Based on this approach, we develop GRAM-R² a generative reward model trained to produce not only preference labels but also accompanying reward rationales. GRAM-R² can serve as a foundation model for reward reasoning and can be applied to a wide range of tasks with minimal or no additional fine-tuning. It can support downstream applications such as policy optimization and task-specific reward tuning. Experiments on response ranking, task adaptation, and reinforcement learning from human feedback demonstrate that GRAM-R² consistently delivers strong performance, outperforming several strong discriminative and generative baselines.

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AAAI Conference 2026 Conference Paper

Probing Preference Representations: A Multi-Dimensional Evaluation and Analysis Method for Reward Models

Chenglong Wang
Yifu Huo
Yang Gan
Yongyu Mu
Qiaozhi He
Murun Yang
Bei Li
Chunliang Zhang

Previous methods evaluate reward models by testing them on a fixed pairwise ranking test set, but they typically do not provide performance information on each preference dimension. In this work, we address the evaluation challenge of reward models by probing preference representations. To confirm the effectiveness of this evaluation method, we construct a Multi-dimensional Reward Model Benchmark (MRMBench), a collection of six probing tasks for different preference dimensions. We design it to favor and encourage reward models that better capture preferences across different dimensions. Furthermore, we introduce an analysis method, inference-time probing, which identifies the dimensions used during the reward prediction and enhances its interpretability. Through extensive experiments, we find that MRMBench strongly correlates with LLM alignment performance, supporting it as a reliable reference for developing advanced reward models. By analyzing the evaluation results on MRMBench, we reveal that reward models struggle to simultaneously capture preferences across multiple dimensions, highlighting the potential of multi-objective optimization in reward modeling. Furthermore, our results demonstrate that the proposed inference-time probing method provides a reliable metric for assessing the confidence of reward predictions, leading to improved alignment of large language models.

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ICML Conference 2025 Conference Paper

GRAM: A Generative Foundation Reward Model for Reward Generalization

Chenglong Wang 0002
Yang Gan
Yifu Huo
Yongyu Mu
Qiaozhi He
Murun Yang
Bei Li
Tong Xiao 0001

In aligning large language models (LLMs), reward models have played an important role, but are standardly trained as discriminative models and rely only on labeled human preference data. In this paper, we explore methods that train reward models using both unlabeled and labeled data. Building on the generative models in LLMs, we develop a generative reward model that is first trained via large-scale unsupervised learning and then fine-tuned via supervised learning. We also show that by using label smoothing, we are in fact optimizing a regularized pairwise ranking loss. This result, in turn, provides a new view of training reward models, which links generative models and discriminative models under the same class of training objectives. The outcome of these techniques is a foundation reward model, which can be applied to a wide range of tasks with little or no further fine-tuning effort. Extensive experiments show that this model generalizes well across several tasks, including response ranking, reinforcement learning from human feedback, and task adaptation with fine-tuning, achieving significant performance improvements over several strong baseline models.

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NeurIPS Conference 2025 Conference Paper

MRO: Enhancing Reasoning in Diffusion Language Models via Multi-Reward Optimization

Chenglong Wang
Yang Gan
Hang Zhou
Chi Hu
Yongyu Mu
Kai Song
Murun Yang
Bei Li

Recent advances in diffusion language models (DLMs) have presented a promising alternative to traditional autoregressive large language models (LLMs). However, DLMs still lag behind LLMs in reasoning performance, especially as the number of denoising steps decreases. Our analysis reveals that this shortcoming arises primarily from the independent generation of masked tokens across denoising steps, which fails to capture the token correlation. In this paper, we define two types of token correlation: intra-sequence correlation and inter-sequence correlation, and demonstrate that enhancing these correlations improves reasoning performance. To this end, we propose a Multi-Reward Optimization (MRO) approach, which encourages DLMs to consider the token correlation during the denoising process. More specifically, our MRO approach leverages test-time scaling, reject sampling, and reinforcement learning to directly optimize the token correlation with multiple elaborate rewards. Additionally, we introduce group step and importance sampling strategies to mitigate reward variance and enhance sampling efficiency. Through extensive experiments, we demonstrate that MRO not only improves reasoning performance but also achieves significant sampling speedups while maintaining high performance on reasoning benchmarks.

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AAAI Conference 2025 Conference Paper

RoVRM: A Robust Visual Reward Model Optimized via Auxiliary Textual Preference Data

Chenglong Wang
Yang Gan
Yifu Huo
Yongyu Mu
Murun Yang
Qiaozhi He
Tong Xiao
Chunliang Zhang

Large vision-language models (LVLMs) often fail to align with human preferences, leading to issues like generating misleading content without proper visual context (also known as hallucination). A promising solution to this problem is using human-preference alignment techniques, such as best-of-n sampling and reinforcement learning. However, these techniques face the difficulty arising from the scarcity of visual preference data, which is required to train a visual reward model (VRM). In this work, we continue the line of research. We present a Robust Visual Reward Model (RoVRM) which improves human-preference alignment for LVLMs. RoVRM leverages auxiliary textual preference data through a three-phase progressive training and optimal transport-based preference data selection to effectively mitigate the scarcity of visual preference data. We experiment with RoVRM on the commonly used vision-language tasks based on the LLaVA-1.5-7B and -13B models. Experimental results demonstrate that RoVRM consistently outperforms traditional VRMs. Furthermore, our three-phase progressive training and preference data selection approaches can yield consistent performance gains over ranking-based alignment techniques, such as direct preference optimization.

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