论文检索

Social Aspects · Trustworthy Machine Learning

Egor Zverev, Evgenii Kortukov, Alexander Panfilov, Alexandra Volkova, Soroush Tabesh, Sebastian Lapuschkin, Wojciech Samek, Christoph Lampert

Despite their remarkable performance, large language models lack elementary safety features, making them susceptible to numerous malicious attacks. In particular, previous work has identified the absence of an intrinsic separation between instructions and data as the root cause of the success of prompt injection attacks. In this work, we propose a new architectural element, ASIDE, that allows language models to clearly separate instructions and data at the level of token embeddings. ASIDE applies an orthogonal rotation to the embeddings of data tokens, thus creating clearly distinct representations of instructions and data tokens without introducing any additional parameters. As we demonstrate experimentally across a range of models, instruction-tuning LLMs with ASIDE (1) achieves substantially higher instruction-data separation without performance loss and (2) makes the models more robust to prompt injection benchmarks, even without dedicated safety training. Additionally, we provide insights into the mechanism underlying our method through an analysis of the model representations.

Applications · Everything Else

Robin Zbinden, Wesley Monteith-Finas, Gencer Sumbul, Nina van Tiel, Chiara Vanalli, Devis Tuia

Multimodal learning is crucial for ecological applications, which rely on heterogeneous data sources (e.g., satellite imagery, environmental time series, tabular predictors, bioacoustics) but often suffer from incomplete data across and within modalities (e.g., unavailable satellite image due to cloud cover, missing records in a time series). While data masking strategies have been used to improve robustness to missing data by exposing models to varying input subsets during training, existing approaches typically rely on static masking and inadequately explore the space of input combinations. As a result, they fail to address modality imbalance, a critical challenge in multimodal learning where dominant modalities hinder the optimization of others. To fill this gap, we introduce Modality Imbalance-Aware Masking (MIAM), a dynamic masking strategy that: (i) explores the full space of input combinations; (ii) prioritizes informative or challenging subsets; and (iii) adaptively increases the masking probability of dominant modalities based on their relative performance and learning dynamics. We evaluate MIAM on two key ecological datasets, GeoPlant and TaxaBench, with diverse modality configurations, and show that MIAM significantly improves robustness and predictive performance over previous masking strategies. In addition, MIAM supports fine-grained contribution analysis across and within modalities, revealing which variables, time segments, or image regions most strongly drive performance.

Applications · Robotics

Xinyan Chen, Qinchun Li, Ruiqin Ma, Jiaqi Bai, Li Yi, Jianfei Yang

Accurate material identification plays a crucial role in embodied AI systems, enabling a wide range of applications. However, current vision-based solutions are limited by the inherent constraints of optical sensors, while radio-frequency (RF) approaches, which can reveal intrinsic material properties, have received growing attention. Despite this progress, RF-based material identification remains hindered by the lack of large-scale public datasets and the limited benchmarking of learning-based approaches. In this work, we present RF-MatID, the first open-source, large-scale, wide-band, and geometry-diverse RF dataset for fine-grained material identification. RF-MatID includes 16 fine-grained categories grouped into 5 superclasses, spanning a broad frequency range from 4 to 43.5 GHz, and comprises 142k samples in both frequency- and time-domain representations. The dataset systematically incorporates controlled geometry perturbations, including variations in incidence angle and stand-off distance. We further establish a multi-setting, multi-protocol benchmark by evaluating state-of-the-art deep learning models, assessing both in-distribution performance and out-of-distribution robustness under cross-angle and cross-distance shifts. The 5 frequency-allocation protocols enable systematic frequency- and region-level analysis, thereby facilitating real-world deployment. RF-MatID aims to enable reproducible research, accelerate algorithmic advancement, foster cross-domain robustness, and support the development of real-world application in RF-based material identification.

Computer Vision · Image and Video Generation

Zhenzhi Wang, Jiaqi Yang, Jianwen Jiang, Chao Liang, Gaojie Lin, Zerong Zheng, Ceyuan Yang, Yuan Zhang, Mingyuan Gao, Dahua Lin

End-to-end human animation with rich multi-modal conditions, e.g., text, image and audio has achieved remarkable advancements in recent years. However, most existing methods could only animate a single subject and inject conditions in a global manner, ignoring scenarios where multiple concepts could appear in the same video with rich human-human interactions and human-object interactions. Such a global assumption prevents precise and per-identity control of multiple concepts including humans and objects, therefore hinders applications. In this work, we discard the single-entity assumption and introduce a novel framework that enforces strong, region‑specific binding of conditions from modalities to each identity's spatiotemporal footprint. Given reference images of multiple concepts, our method could automatically infer layout information by leveraging a mask predictor to match appearance cues between the denoised video and each reference appearance. Furthermore, we inject local audio condition into its corresponding region to ensure layout-aligned modality matching in an iterative manner. This design enables the high-quality generation of human dialogue videos between two to three people or video customization from multiple reference images. Empirical results and ablation studies validate the effectiveness of our explicit layout control for multi-modal conditions compared to implicit counterparts and other existing methods.

Jianwen Jiang, Weihong Zeng, Zerong Zheng, Jiaqi Yang, Chao Liang, Wang Liao, Han Liang, Weifeng Chen, XING WANG, Yuan Zhang 等

Current video avatar models can generate fluid animations but struggle to capture a character's authentic essence, primarily synchronizing motion with low-level audio cues instead of understanding higher-level semantics like emotion or intent. To bridge this gap, we propose a novel framework for generating character animations that are not only physically plausible but also semantically rich and expressive. Our model is built on two technical innovations. First, we employ Multimodal Large Language Models to generate a structured textual representation from input conditions, providing high-level semantic guidance for creating contextually and emotionally resonant actions. Second, to ensure robust fusion of multimodal signals, we introduce a specialized Multimodal Diffusion Transformer architecture featuring a novel Pseudo Last Frame design. This allows our model to accurately interpret the joint semantics of audio, images and text, generating motions that are deeply coherent with the overall context. Comprehensive experiments validate the superiority of our method, which achieves compelling results in lip-sync accuracy, video quality, motion naturalness, and semantic consistency. The approach also shows strong generalization to challenging scenarios, including multi-person and non-human subjects. Our video results are linked in https://omnihuman-lab.github.io/v1_5/ .

Computer Vision · Image and Video Generation

Jianwen Jiang, Weihong Zeng, Zerong Zheng, Jiaqi Yang, Chao Liang, Wang Liao, Han Liang, Weifeng Chen, XING WANG, Yuan Zhang 等

Current video avatar models can generate fluid animations but struggle to capture a character's authentic essence, primarily synchronizing motion with low-level audio cues instead of understanding higher-level semantics like emotion or intent. To bridge this gap, we propose a novel framework for generating character animations that are not only physically plausible but also semantically rich and expressive. Our model is built on two technical innovations. First, we employ Multimodal Large Language Models to generate a structured textual representation from input conditions, providing high-level semantic guidance for creating contextually and emotionally resonant actions. Second, to ensure robust fusion of multimodal signals, we introduce a specialized Multimodal Diffusion Transformer architecture featuring a novel Pseudo Last Frame design. This allows our model to accurately interpret the joint semantics of audio, images and text, generating motions that are deeply coherent with the overall context. Comprehensive experiments validate the superiority of our method, which achieves compelling results in lip-sync accuracy, video quality, motion naturalness, and semantic consistency. The approach also shows strong generalization to challenging scenarios, including multi-person and non-human subjects. Our video results are linked in https://omnihuman-lab.github.io/v1_5/ .

Applications · Neuroscience, Cognitive Science

Melika Honarmand, Ayati Sharma, Badr AlKhamissi, Johannes Mehrer, Martin Schrimpf

Dyslexia, a neurodevelopmental disorder characterized by persistent reading difficulties, is often linked to reduced activity of the visual word form area (VWFA) in the ventral occipito-temporal cortex. Traditional approaches to studying dyslexia, such as behavioral and neuroimaging methods, have provided valuable insights but remain limited in their ability to test causal hypotheses about the underlying mechanisms of reading impairments. In this study, we use large-scale vision-language models (VLMs) to simulate dyslexia by functionally identifying and perturbing artificial analogues of word processing. Using stimuli from cognitive neuroscience, we identify visual-word-form-selective units within VLMs and demonstrate that they predict human VWFA neural responses. Ablating model VWF units leads to selective impairments in reading tasks while general visual and language comprehension abilities remain intact. In particular, the resulting model matches dyslexic humans' phonological deficits without a significant change in orthographic processing, and mirrors dyslexic behavior in font sensitivity. Taken together, our modeling results replicate key characteristics of dyslexia and establish a computational framework for investigating brain disorders.

Applications · Health

Xinyao Wu, Zhe Xu, Raymond Kai-yu Tong

Class-incremental learning (CIL) in medical image-guided diagnosis requires models to preserve knowledge of historical disease classes while adapting to emerging categories. Pre-trained models (PTMs) with well-generalized features provide a strong foundation, yet most PTM-based CIL strategies, such as prompt tuning, task-specific adapters and model mixtures, rely on increasingly complex designs. While effective in general-domain benchmarks, these methods falter in medical imaging, where low intra-class variability and high inter-domain shifts (from scanners, protocols and institutions) make CIL particularly prone to representation collapse and domain misalignment. Under such conditions, we find that lightweight representation calibration strategies, often dismissed in general-domain CIL for their modest gains, can be remarkably effective for adapting PTMs in medical settings. To this end, we introduce Random Anchors with Low-rank Decorrelated Learning (RA-LDL), a minimalist representation-based framework that combines (a) PTM-based feature extraction with optional ViT-Adapter tuning, (b) feature calibration via frozen Random Anchor projection and a single-session-trained Low-Rank Projection (LRP), and (c) analytical closed-form decorrelated learning. The entire pipeline requires only one training session and minimal task-specific tuning, making it appealing for efficient deployment. Despite its simplicity, RA-LDL achieves consistent and substantial improvements across both general-domain and medical-specific PTMs, and outperforms recent state-of-the-art methods on four diverse medical imaging datasets. These results highlight that minimalist representation recalibration, rather than complex architectural modifications, can unlock the underexplored potential of PTMs in medical CIL. We hope this work establishes a practical and extensible foundation for future research in class-incremental image-guided diagnosis. Code will be made publicly available.

Reinforcement Learning · Deep RL

Shenao Zhang, Donghan Yu, Yihao Feng, Bowen Jin, Zhaoran Wang, John Peebles, Zirui Wang

Large language models excel with reinforcement learning (RL), but fully unlocking this potential requires a mid-training stage. Intuitively, an effective mid-training stage should both learn a strong policy prior and enable fast learning through online interactions. We formalize this intuition by presenting the first theoretical result on how mid-training shapes post-training: it acquires strong policy priors by efficiently pruning the action space and accelerates RL convergence by shortening the effective planning horizon. Moreover, we prove that temporal abstractions simultaneously compress the size of the action set and reduce the decision horizon, thereby improving regret minimization after training. Building on these insights, we introduce Reasoning as Action Abstractions (RA3), a scalable mid-training algorithm. Specifically, we derive a temporal variational bound and optimize it by iteratively discovering temporally-consistent latent structures via RL, then fine-tuning on the bootstrapped data. Experiments on code generation tasks demonstrate the effectiveness of our approach. Across multiple base models, RA3 improves the average performance on HumanEval and MBPP by 8 and 4 points over the base model and the next-token prediction baseline. Furthermore, RA3 achieves faster convergence and higher asymptotic performance in RLVR on HumanEval+, MBPP+, LiveCodeBench, and Codeforces.

Theory · Interpretability and Visualization

Yoav Gur-Arieh, Mor Geva, Atticus Geiger

A key component of in-context reasoning is the ability of language models (LMs) to bind entities for later retrieval. For example, an LM might represent *Ann loves pie* by binding *Ann* to *pie*, allowing it to later retrieve *Ann* when asked *Who loves pie?* Prior research on short lists of bound entities found strong evidence that LMs implement such retrieval via a **positional mechanism**, where *Ann* is retrieved based on its position in context. In this work, we find that this mechanism generalizes poorly to more complex settings; as the number of bound entities in context increases, the positional mechanism becomes noisy and unreliable in middle positions. To compensate for this, we find that LMs supplement the positional mechanism with a **lexical mechanism** (retrieving *Ann* using its bound counterpart *pie*) and a **reflexive mechanism** (retrieving *Ann* through a direct pointer). Through extensive experiments on nine models and ten binding tasks, we uncover a consistent pattern in how LMs mix these mechanisms to drive model behavior. We leverage these insights to develop a causal model combining all three mechanisms that estimates next token distributions with 95\% agreement. Finally, we show that our model generalizes to substantially longer inputs of open-ended text interleaved with entity groups, further demonstrating the robustness of our findings in more natural settings. Overall, our study establishes a more complete picture of how LMs bind and retrieve entities in-context.

General Machine Learning · Representation Learning

Jaehyeong Jeong, Hee-Jun Jung, Kangil Kim

Combinatorial generalization (CG)—generalizing to unseen combinations of known semantic factors—remains a grand challenge in machine learning. While symmetry-based methods are promising, they learn from observed data and thus fail at what we term $\textbf{symmetry generalization}$: extending learned symmetries to novel data. We tackle this by proposing a novel framework that endows the latent space with the structure of a $\textbf{symmetric space}$, a class of manifolds whose geometric properties provide a principled way to extend these symmetries. Our method operates in two steps: first, it imposes this structure by learning the underlying algebraic properties via the $\textbf{Cartan decomposition}$ of a learnable Lie algebra. Second, it uses $\textbf{geodesic symmetry}$ as a powerful self-supervisory signal to ensure this learned structure extrapolates from observed samples to unseen ones. A detailed analysis on a synthetic dataset validates our geometric claims, and experiments on standard CG benchmarks show our method significantly outperforms existing approaches.

Reinforcement Learning · Deep RL

Yubo Dong, Hehe Fan, Linchao Zhu, Yi Yang

Recent Large Language Models (LLMs) have made remarkable progress, but they still struggle with complex reasoning tasks such as logical deduction and planning. This is partly because they rely primarily on token-level probability relationships, which limits their ability to reason effectively. In this paper, inspired by cognitive science and neurosymbolic AI, we introduce **Structured Reasoning**, which aimes at enhancing the reasoning capabilities of LLMs from the step level. To this end, we first collect high‑frequency, domain‑agnostic reasoning step tags and construct a structured reasoning dataset with those tags. Then, we treat a reasoning process as a *directed acyclic graph*, where the vertices represent steps and the edges indicate the direction of reasoning. In this context, an efficient reasoning process corresponds to, or can be characterized by, a sparse reasoning graph. To construct reasoning graphs, we introduce *structured tags* for reliable step extraction from LLM outputs. For single-graph optimization, we propose the *MaxFlow reward*, which rewards graphs with balanced node contributions and fewer redundant steps. The quality of a sparse reasoning graph can be reflected by the total flow from all steps to the final answer. For multi-graph comparison, we propose the *LCS reward*, which selects reliable reasoning paths by identifying optimal common subsequences (consecutive steps) shared across multiple generated responses (sequences). Experiments with DeepSeek-R1-Distill-Qwen-1.5B and 7B models show that our method consistently outperforms GRPO and other carefully tuned baselines across various context lengths (0.5k–8k). Structured Reasoning shows particular strength in efficiency (better performance with fewer steps) and stability (consistently generating high-quality outputs across a temperature range of 0.1 to 1.0). Methods and examples is currently available on our website: https://cnsdqd-dyb.github.io/structured-reasoning.

Deep Learning · Everything Else

Adnan El Assadi, Isaac Chung, Roman Solomatin, Niklas Muennighoff, Kenneth Enevoldsen

Comparing human and model performance offers a valuable perspective for understanding the strengths and limitations of embedding models, highlighting where they succeed and where they fail to capture meaning and nuance. However, such comparisons are rarely made, as human performance on embedding tasks is difficult to measure. To fill this gap, we introduce HUME: Human Evaluation Framework for Text Embeddings. While frameworks like MTEB provide broad model evaluation, they lack reliable estimates of human performance, limiting the interpretability of model scores. We measure human performance across 16 MTEB datasets spanning reranking, classification, clustering, and semantic textual similarity across linguistically diverse high- and low-resource languages. Humans achieve an average performance of 77.6% compared to 80.1% for the best embedding model, though with substantial variation: models reach high performance on some datasets while struggling on notably low-resource languages. Our human annotations also reveal multiple dataset issues. We additionally benchmark nine LLMs as annotators on reranking, classification, and STS tasks, finding that they fall short of human performance (76.1% vs. 81.2%) despite offering scalability advantages. We provide human performance baselines, insights into task difficulty patterns, and an extensible evaluation framework that enables a more meaningful interpretation of results and informs the development of both models and benchmarks. Our code, dataset, and leaderboard are publicly available at https://github.com/embeddings-benchmark/mteb.

Ruiyi Fang, Jingyu Zhao, Shuo Wang, Ruizhi Pu, Bingheng Li, Jiale Cai, Zhihao Li, Zihao Jing, Jian Zhu, Song Tang 等

Graph domain adaptation (GDA) transfers knowledge from a labeled source graph to an unlabeled target graph to alleviate label scarcity. In multi-view graphs, the challenge of mitigating domain shift is constrained by structural information across various views. Moreover, within each view, structures at different hops capture distinct neighborhood levels, which can lead to varying structural discrepancies. However, existing methods typically assume only a single-view graph structure, which cannot effectively capture the rich structural information in multi-relational graphs and hampers adaptation performances. In this paper, we tackle the challenging Multi-view Graph Domain Adaptation (MGDA) problem by proposing Structural Aggregation Guided Alignment (SAGA) that aligns multi-view graph data via dynamic view and neighborhood order selection. Specifically, we propose the notion of Structural Aggregation Distance (SAD) as a dynamic discrepancy metric that jointly considers view and neighborhood order, allowing the dominant view–order pair to vary during training. Through empirical analysis, we justify the validity of SAD and show that domain discrepancy in MGDA is largely governed by the dominant view–order pair, which evolves throughout training. Motivated by this observation, we design SAGA, which leverages SAD to dynamically identify the principal view-order pair that guides alignment, thereby effectively characterizing and mitigating both view- and hop-level structural discrepancies between multi-view graphs. Experimental results on various multi-relational graph benchmarks verify the effectiveness of our method.

Social Aspects · Trustworthy Machine Learning

Zhihao Li, Gezheng Xu, Jiale Cai, Ruiyi Fang, Di Wu, Qicheng Lao, Charles Ling, Boyu Wang

Unlearnable Examples (UEs) serve as a data protection strategy that generates imperceptible perturbations to mislead models into learning spurious correlations instead of underlying semantics. In this paper, we uncover a fundamental vulnerability of UEs that emerges when learning starts from a pretrained model. Crucially, our empirical analysis shows that even when data are protected by carefully crafted perturbations, pretraining priors still furnish rich semantic representations that allow the model to circumvent the shortcuts introduced by UEs and capture genuine features, thereby nullifying unlearnability. To address this, we propose $\textbf{BAIT}$ ($\textbf{B}$inding $\textbf{A}$rtificial perturbations to $\textbf{I}$ncorrect $\textbf{T}$argets), a novel bi‑level optimization formulation. Specifically, the inner level aims at associating the perturbed samples with real labels to simulate standard data-label alignment, while the outer level actively disrupts this alignment by enforcing a mislabel-perturbation binding that maps samples to designated incorrect targets. This mechanism effectively overrides the semantic guidance of priors, forcing the model to rely on the injected perturbations and consequently preventing the acquisition of true semantics. Extensive experiments on standard benchmarks and multiple pretrained backbones demonstrate that BAIT effectively mitigates the influence of pretraining priors and maintains data unlearnability. Code is available at https://github.com/zhli-cs/BAIT.

Mark Goldstein, Anshuk Uppal, Raghav Singhal, Aahlad Manas Puli, Rajesh Ranganath

Diffusion and flow-based models benefit from simple regression losses, but inference (i.e, producing samples) incurs significant computational overhead because it requires integration. Consistency models address this overhead by directly learning the flow maps along the ODE trajectory, revealing a design space for the learning problem between one-step and many-step approaches. However, existing consistency training methods feature computational challenges such as requiring model inverses or backpropagation through iterated model calls, and do not always prove that the desired ODE flow map is a solution to the loss. We introduce SGFlow, an approach for learning flow maps that bypasses explicit invertibility constraints and expensive differentiation through model iteration. SGFlow trains a model to compute both the ODE solutions and the implied velocity from scratch by following non-conservative dynamics with a stationary point at the desired flow map. On the CIFAR image benchmark, SGFlow attains a favorable relationship of FID to step count, relative to flow matching, MeanFlow, and several other flow map learning methods.

Computer Vision · Image and Video Generation

Zhuoran Yang, Yanyong Zhang

Autonomous driving relies on robust models trained on large-scale, high-quality multi-view driving videos. Although world models provide a cost-effective solution for generating realistic driving data, they often suffer from identity drift, where the same object changes its appearance or category across frames due to the absence of instance-level temporal constraints. We introduce **ConsisDrive**, an identity-preserving driving world model designed to enforce temporal consistency at the instance level. Our framework incorporates two key components: (1) Instance-Masked Attention, which applies instance identity masks and trajectory masks within attention blocks to ensure that visual tokens interact only with their corresponding instance features across spatial and temporal dimensions, thereby preserving object identity consistency; and (2) Instance-Masked Loss, which adaptively emphasizes foreground regions with probabilistic instance masking, reducing background noise while maintaining overall scene fidelity. By integrating these mechanisms, ConsisDrive achieves state-of-the-art driving video generation quality and demonstrates significant improvements in downstream autonomous driving tasks on the nuScenes dataset.

Deep Learning · Everything Else

Filip Szatkowski, Patryk Będkowski, Alessio Devoto, Jan Dubiński, Pasquale Minervini, Mikolaj Piorczynski, Simone Scardapane, Bartosz Wójcik

Activation sparsity is an intriguing property of deep neural networks that has been extensively studied in ReLU-based models, due to its advantages for efficiency, robustness, and interpretability. However, methods relying on exact zero activations do not directly apply to modern Large Language Models (LLMs), leading to fragmented, model-specific strategies for LLM activation sparsity and a gap in its general understanding. In this work, we introduce a general framework for evaluating sparsity robustness in contemporary LLMs and conduct a systematic investigation of this phenomenon in their feedforward~(FFN) layers. Our results uncover universal properties of activation sparsity across diverse model families and scales. Importantly, we observe that the potential for effective activation sparsity grows with model size, highlighting its increasing relevance as models scale. Furthermore, we present the first study of activation sparsity in diffusion-based LLMs. Overall, our work provides a comprehensive perspective and practical guidance for harnessing activation sparsity in LLM design and acceleration.

Applications · Time Series

Tong Guan, Zijie Meng, Dianqi Li, Shiyu Wang, Chao-Han Huck Yang, Qingsong Wen, Zuozhu Liu, Sabato Siniscalchi, Ming Jin, Shirui Pan

Recent advances in multimodal time series learning underscore a paradigm shift from analytics centered on basic patterns toward advanced time series understanding and reasoning. However, existing multimodal time series datasets mostly remain at the level of surface alignment and question answering, without reaching the depth of genuine reasoning. The absence of well-defined tasks that genuinely require time series reasoning, along with the scarcity of high-quality data, has limited progress in building practical time series reasoning models (TSRMs). To this end, we introduce Time Series Reasoning Suite (TSR-Suite), which formalizes four atomic tasks that span three fundamental capabilities for reasoning with time series: (1) perception, acquired through scenario understanding and causality discovery; (2) extrapolation, realized via event-aware forecasting; and (3) decision-making, developed through deliberation over perception and extrapolation. TSR-Suite is the first comprehensive time series reasoning suite that supports not only thorough evaluation but also the data pipeline and training of TSRMs. It contains more than 23K samples, of which 2.3K are carefully curated through a human-guided hierarchical annotation process. Building on this foundation, we introduce TimeOmni-1, the first unified reasoning model designed to address diverse real-world problems demanding time series reasoning. The model is trained in multiple stages, integrating a mixture of task scenarios, novel reward functions, and tailored optimizations. Experiments show that TimeOmni-1 delivers strong out-of-distribution generalization across all tasks and achieves a high rate of valid responses. It significantly improves causality discovery accuracy (64.0% vs. 35.9% with GPT-4.1) and raises the valid response rate by over 6% compared to GPT-4.1 on the event-aware forecasting task.

Reinforcement Learning · Deep RL

Xiusi Chen, Gaotang Li, Ziqi Wang, Bowen Jin, Cheng Qian, Yu Wang, Hongru WANG, Yu Zhang, Denghui Zhang, Tong Zhang 等

Reward modeling is essential for aligning large language models with human preferences through reinforcement learning. To provide accurate reward signals, a reward model (RM) should stimulate deep thinking and conduct interpretable reasoning before assigning a score or a judgment. Inspired by recent advances of long chain-of-thought on reasoning-intensive tasks, we hypothesize and validate that integrating reasoning into reward modeling significantly enhances RM's interpretability and performance. We introduce a new class of generative reward models, Reasoning Reward Models (ReasRMs), which formulate reward modeling as a reasoning task. We propose a reasoning-oriented training pipeline and train a family of ReasRMs, RM-R1. RM-R1 features a chain-of-rubrics (CoR) mechanism -- self-generating sample-level chat rubrics or math/code solutions, and evaluating candidate responses against them. The training of RM-R1 consists of two key stages: (1) distillation of high-quality reasoning chains and (2) reinforcement learning with verifiable rewards. Empirically, our models achieve superior performance across three reward model benchmarks on average, outperforming much larger open-weight models (e.g., INF-ORM-Llama3.1-70B) and proprietary ones (e.g., GPT-4o) by up to 4.9%. Beyond final performance, we perform thorough analyses to understand the key ingredients of successful ReasRM training.