# AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection

arXiv: https://arxiv.org/abs/2504.11914 · HTML: https://mlsanity.com/paper/2504.11914/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Placed in Chain-of-thought reasoning for driving, one of 17 directions in LLM reasoning.
- Builds on 26 of its 63 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Placed in

- LLM reasoning / [Chain-of-thought reasoning for driving](https://mlsanity.com/lr/chain-of-thought-reasoning-for-driving/)

## Builds on (most cited first, 26 of 26)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024)
- [LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models](https://mlsanity.com/paper/2407.07895/) (2024)
- [InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model](https://mlsanity.com/paper/2401.16420/) (2024)
- [Sub-Image Anomaly Detection with Deep Pyramid Correspondences](https://mlsanity.com/paper/2005.02357/) (2020)
- [SPHINX: The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal Large Language Models](https://mlsanity.com/paper/2311.07575/) (2023)
- [AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection](https://mlsanity.com/paper/2310.18961/) (2023)
- [The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization](https://mlsanity.com/paper/2112.09045/) (2021)
- [AnomalyGPT: Detecting Industrial Anomalies Using Large Vision-Language Models](https://mlsanity.com/paper/2308.15366/) (2023)
- [Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection](https://mlsanity.com/paper/2405.14325/) (2024)
- [Maybe Only 0.5% Data is Needed: A Preliminary Exploration of Low Training Data Instruction Tuning](https://mlsanity.com/paper/2305.09246/) (2023)
- [Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models](https://mlsanity.com/paper/2502.07601/) (2025)
- [Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection](https://mlsanity.com/paper/2310.19070/) (2023)
- [LogicAD: Explainable Anomaly Detection via VLM-based Text Feature Extraction](https://mlsanity.com/paper/2501.01767/) (2025)
- [Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning](https://mlsanity.com/paper/2403.11083/) (2024)
- [A Few More Examples May Be Worth Billions of Parameters](https://mlsanity.com/paper/2110.04374/) (2021)
- [Can Multimodal Large Language Models be Guided to Improve Industrial Anomaly Detection?](https://mlsanity.com/paper/2501.15795/) (2025)
- [EIAD: Explainable Industrial Anomaly Detection Via Multi-Modal Large Language Models](https://mlsanity.com/paper/2503.14162/) (2025)
- [A Survey on Foundation-Model-Based Industrial Defect Detection](https://mlsanity.com/paper/2502.19106/) (2025)

## Built on by (most cited first, 28 of 28)

- [OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning](https://mlsanity.com/paper/2505.22039/) (2025)
- [IAD-R1: Reinforcing Consistent Reasoning in Industrial Anomaly Detection](https://mlsanity.com/paper/2508.09178/) (2025)
- [EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO](https://mlsanity.com/paper/2507.21619/) (2025)
- [LR-IAD:Mask-Free Industrial Anomaly Detection with Logical Reasoning](https://mlsanity.com/paper/2504.19524/) (2025)
- [Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning](https://mlsanity.com/paper/2602.09850/) (2026)
- [AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation](https://mlsanity.com/paper/2512.13671/) (2025)
- [JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA](https://mlsanity.com/paper/2605.20284/) (2026)
- [Reasoning-Driven Anomaly Detection and Localization with Image-Level Supervision](https://mlsanity.com/paper/2603.27179/) (2026)
- [EAGLE: Expert-Augmented Attention Guidance for Tuning-Free Industrial Anomaly Detection in Multimodal Large Language Models](https://mlsanity.com/paper/2602.17419/) (2026)
- [IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools](https://mlsanity.com/paper/2605.20682/) (2026)
- [ABounD: Adversarial Boundary-Driven Few-Shot Learning for Multi-Class Anomaly Detection](https://mlsanity.com/paper/2511.22436/) (2025)
- [UniPCB: A Unified Vision-Language Benchmark for Open-Ended PCB Quality Inspection](https://mlsanity.com/paper/2601.19222/) (2026)
- [M3-AD: Reflection-aware Multi-modal, Multi-category, and Multi-dimensional Benchmark and Framework for Industrial Anomaly Detection](https://mlsanity.com/paper/2603.00055/) (2026)
- [Topo-R1: Detecting Topological Anomalies via Vision-Language Models](https://mlsanity.com/paper/2603.13054/) (2026)
- [AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection](https://mlsanity.com/paper/2605.30140/) (2026)
- [CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection](https://mlsanity.com/paper/2607.02930/) (2026)
- [OPD-IAD: From Language Judgment to Industrial Anomaly Detection via On-Policy Self-Distillation](https://mlsanity.com/paper/2607.18850/) (2026)
- [Robust Object Detection for Autonomous Driving via Curriculum-Guided Group Relative Policy Optimization](https://mlsanity.com/paper/2509.22688/) (2025)
- [GATE-AD: Graph Attention Network Encoding For Few-Shot Industrial Visual Anomaly Detection](https://mlsanity.com/paper/2603.15300/) (2026)
- [MMVIAD: Multi-view Multi-task Video Understanding for Industrial Anomaly Detection](https://mlsanity.com/paper/2605.10833/) (2026)
- [DifferAD-R1: A Difference-Guided IndustrialAnomaly Localization with Multimodal LargeLanguage Models](https://mlsanity.com/paper/2606.16601/) (2026)
- [From Structure to Synergy: A Survey of Vision-Language Perception Paradigm Evolution in Multimodal Large Language Models](https://mlsanity.com/paper/2606.26196/) (2026)
- [Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection](https://mlsanity.com/paper/2607.03817/) (2026)
- [ADOPD: Reference-Privileged On-Policy Distillation for MLLM-Based Industrial Anomaly Detection](https://mlsanity.com/paper/2608.09789/) (2026)
- [Absence is Presence: Understanding Visual Scene Negative Events Under Safety Cognitive Constraint](https://mlsanity.com/paper/2609.19812/) (2026)
- [Industrial Anomaly Detection via Defect-Grounded Reasoning in Visual Latent Space](https://mlsanity.com/paper/2609.29457/) (2026)
- [TED:Text-Axis Evidence Decomposition for Prompted Anomaly Localization](https://mlsanity.com/paper/2609.39033/) (2026)
- [Is In-Domain Training Enough for Fine-Grained Industrial Anomaly Understanding?](https://mlsanity.com/paper/2610.12310/) (2026)

## Abstract (arXiv)

Industrial Anomaly Detection (IAD) poses a formidable challenge due to the scarcity of defective samples, making it imperative to deploy models capable of robust generalization to detect unseen anomalies effectively. Traditional approaches, often constrained by hand-crafted features or domain-specific expert models, struggle to address this limitation, underscoring the need for a paradigm shift. We introduce AnomalyR1, a pioneering framework that leverages VLM-R1, a Multimodal Large Language Model (MLLM) renowned for its exceptional generalization and interpretability, to revolutionize IAD. By integrating MLLM with Group Relative Policy Optimization (GRPO), enhanced by our novel Reasoned Outcome Alignment Metric (ROAM), AnomalyR1 achieves a fully end-to-end solution that autonomously processes inputs of image and domain knowledge, reasons through analysis, and generates precise anomaly localizations and masks. Based on the latest multimodal IAD benchmark, our compact 3-billion-parameter model outperforms existing methods, establishing state-of-the-art results. As MLLM capabilities continue to advance, this study is the first to deliver an end-to-end VLM-based IAD solution that demonstrates the transformative potential of ROAM-enhanced GRPO, positioning our framework as a forward-looking cornerstone for next-generation intelligent anomaly detection systems in industrial applications with limited defective data.