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AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection
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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.
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Cited by 5 Pith papers
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Global Logic and Local Search: Dual-Stream Multimodal In-Context Learning for Verifiable Industrial Anomaly Detection
A training-free dual-stream multimodal framework (PVLA + SAM 3 global logic + MCTS local search) improves verifiable industrial anomaly QA without defective training samples.
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CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection
A private-plus-shared LoRA MoE with layer-adaptive momentum transfer enables continual anomaly detection on MLLMs and beats prior continual-learning baselines across class, domain, and modality shifts.
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AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization
AD-FM combines multi-stage reasoning with localization-aware rewards to fine-tune MLLMs for anomaly detection, improving average accuracy by about 22 percentage points over the base model.
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EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO
A difficulty-aware GRPO training scheme with response resampling, advantage reweighting, GPT-generated text samples, and heatmap-guided contrastive embeddings improves InternVL3-8B by 7.77 percentage points on the MMA...
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OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
OmniAD unifies industrial anomaly detection and understanding in a single multimodal model using text-encoded masks and reinforcement learning, reporting 79.1 on MMAD and strong detection scores.
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