Any entity in the visual world can be hierarchically grouped based on shared characteristics and mapped to fine-grained sub-categories. While Multi-modal Large Language Models (MLLMs) achieve strong performance on coarse-grained visual tasks, they often struggle with Fine-Grained Visual Recognition (FGVR). Adapting general-purpose MLLMs to FGVR typically requires large amounts of annotated data, which is costly to obtain, leaving a substantial performance gap compared to contrastive CLIP models dedicated for discriminative tasks. Moreover, MLLMs tend to overfit to seen sub-categories and generalize poorly to unseen ones. To address these challenges, we propose Fine-R1, an MLLM tailored for FGVR through an R1-style training framework: (1) Chain-of-Thought Supervised Fine-tuning, where we construct a high-quality FGVR CoT dataset with rationales of "visual analysis, candidate sub-categories, comparison, and prediction", transition the model into a strong open-world classifier; and (2) Triplet Augmented Policy Optimization, where Intra-class Augmentation mixes trajectories from anchor and positive images within the same category to improve robustness to intra-class variance, while Inter-class Augmentation maximizes the response distinction conditioned on images across sub-categories to enhance discriminative ability. With only 4-shot training, Fine-R1 outperforms existing general MLLMs, reasoning MLLMs, and even contrastive CLIP models in identifying both seen and unseen sub-categories, showing promise in working in knowledge-intensive domains where gathering expert annotations for all sub-categories is arduous. Code is available at https://github.com/PKU-ICST-MIPL/FineR1_ICLR2026.

翻译：视觉世界中的任何实体均可基于共享特征进行层次化分组，并映射至细粒度子类别。尽管多模态大语言模型（MLLMs）在粗粒度视觉任务上表现出色，但在细粒度视觉识别（FGVR）中往往表现欠佳。将通用MLLMs适配至FGVR通常需要大量标注数据，其获取成本高昂，导致其性能与专为判别任务设计的对比式CLIP模型存在显著差距。此外，MLLMs容易对已见子类别过拟合，并对未见子类别的泛化能力较差。为应对这些挑战，我们提出Fine-R1——一种通过R1式训练框架专为FGVR定制的MLLM：（1）思维链监督微调：我们构建包含“视觉分析、候选子类别、比较与预测”推理过程的高质量FGVR思维链数据集，将模型转化为强大的开放世界分类器；（2）三元组增强策略优化：其中类内增强通过混合同一类别内锚点图像与正样本图像的轨迹来提升对类内方差的鲁棒性，而类间增强则通过最大化跨子类别图像条件下的响应差异来增强判别能力。仅需4样本训练，Fine-R1在识别已见与未见子类别任务上均超越现有通用MLLMs、推理型MLLMs乃至对比式CLIP模型，展现出在难以获取全子类别专家标注的知识密集型领域中的应用潜力。代码发布于https://github.com/PKU-ICST-MIPL/FineR1_ICLR2026。