With the rapid growth of e-commerce, exploring general representations rather than task-specific ones has attracted increasing attention. Although recent multimodal large language models (MLLMs) have driven significant progress in product understanding, they are typically employed as feature extractors that implicitly encode product information into global embeddings, thereby limiting their ability to capture fine-grained attributes. Therefore, we argue that leveraging the reasoning capabilities of MLLMs to explicitly model fine-grained product attributes holds significant potential. Nevertheless, achieving this goal remains non-trivial due to several key challenges: (i) long-context reasoning tends to dilute the model's attention to salient information in the raw input; (ii) supervised fine-tuning (SFT) primarily encourages rigid imitation, limiting the exploration of effective reasoning strategies; and (iii) fine-grained details are progressively attenuated during forward propagation. To address these issues, we propose MOON3.0, the first reasoning-aware MLLM-based model for product representation learning. Our method (1) employs a multi-head modality fusion module to adaptively integrate raw signals; (2) incorporates a joint contrastive and reinforcement learning framework to autonomously explore more effective reasoning strategies; and (3) introduces a fine-grained residual enhancement module to progressively preserve local details throughout the network. Additionally, we release a large-scale multimodal e-commerce benchmark MBE3.0. Experimentally, our model demonstrates state-of-the-art zero-shot performance across various downstream tasks on both our benchmark and public datasets.

翻译：随着电子商务的快速发展，探索通用表示而非任务特定表示已引起广泛关注。尽管近年来的多模态大语言模型（MLLMs）在商品理解领域取得了显著进展，但它们通常作为特征提取器，将商品信息隐式编码为全局嵌入，从而限制了其捕捉细粒度属性的能力。因此，我们认为利用MLLMs的推理能力显式建模细粒度商品属性具有重要潜力。然而，实现这一目标仍面临若干关键挑战：（i）长上下文推理易稀释模型对原始输入中显著信息的注意力；（ii）监督微调（SFT）主要鼓励刚性模仿，限制了对有效推理策略的探索；（iii）细粒度细节在前向传播过程中逐渐衰减。针对上述问题，我们提出MOON3.0——首个基于推理感知MLLM的商品表示学习模型。本方法：（1）采用多头模态融合模块自适应整合原始信号；（2）引入联合对比学习与强化学习框架，自主探索更有效的推理策略；（3）提出细粒度残差增强模块，在网络中逐步保留局部细节。此外，我们发布了大规模多模态电商基准数据集MBE3.0。实验表明，我们的模型在自有基准及公开数据集的多项下游任务中均达到零样本场景下的最优性能。