Pixel-wise capabilities are essential for building interactive intelligent systems. However, pixel-wise multi-modal LLMs (MLLMs) remain difficult to scale due to complex region-level encoders, specialized segmentation decoders, and incompatible training objectives. To address these challenges, we present SAMTok, a discrete mask tokenizer that converts any region mask into two special tokens and reconstructs the mask using these tokens with high fidelity. By treating masks as new language tokens, SAMTok enables base MLLMs (such as the QwenVL series) to learn pixel-wise capabilities through standard next-token prediction and simple reinforcement learning, without architectural modifications and specialized loss design. SAMTok builds on SAM2 and is trained on 209M diverse masks using a mask encoder and residual vector quantizer to produce discrete, compact, and information-rich tokens. With 5M SAMTok-formatted mask understanding and generation data samples, QwenVL-SAMTok attains state-of-the-art or comparable results on region captioning, region VQA, grounded conversation, referring segmentation, scene graph parsing, and multi-round interactive segmentation. We further introduce a textual answer-matching reward that enables efficient reinforcement learning for mask generation, delivering substantial improvements on GRES and GCG benchmarks. Our results demonstrate a scalable and straightforward paradigm for equipping MLLMs with strong pixel-wise capabilities. Our code and models are available.

翻译：像素级能力是构建交互式智能系统的关键。然而，由于复杂的区域级编码器、专用的分割解码器以及不兼容的训练目标，像素级多模态大语言模型（MLLMs）难以扩展。为解决这些挑战，我们提出SAMTok——一种离散掩码分词器，能将任意区域掩码转换为两个特殊标记，并利用这些标记以高保真度重建掩码。通过将掩码视为新语言标记，SAMTok使基础MLLM（如QwenVL系列）无需架构修改和专用损失设计，即可通过标准下一个标记预测与简单强化学习获得像素级能力。SAMTok基于SAM2构建，使用掩码编码器和残差向量量化器在2.09亿个多样化掩码上训练，生成离散、紧凑且信息丰富的标记。借助500万个SAMTok格式的掩码理解与生成数据样本，QwenVL-SAMTok在区域描述、区域视觉问答、基础对话、指代分割、场景图解析及多轮交互分割任务中达到最优或可比结果。我们进一步引入文本答案匹配奖励，实现高效的掩码生成强化学习，在GRES和GCG基准上取得显著改进。研究结果表明，这是一种可扩展且简洁的范式，使MLLM具备强大的像素级能力。我们的代码与模型已开源。