这篇论文用4B小模型在具身推理上打败了7B的Mimo-Embodied，平均提高12%，靠的是把每一步推理都牢牢钉在视觉证据上，挺有意思。
RoboPIN提出PinCoT（固定思维链）方法，将每个推理步骤绑定到视觉锚点，每个锚点包含实体名称、唯一标识、视图索引和空间定位。基于4B参数的小模型，在14个基准测试（涵盖空间推理、多视图推理和指向任务）中，平均超越7B开源模型Mimo-Embodied达12%。该方法通过三阶段后训练注入具身知识和过程监督，显著提升定位精度和跨步骤实体一致性。
RoboPIN: Grounded Embodied Reasoning via Pinned Chain-of-Thought
Embodied reasoning requires models to perceive task-relevant objects and spaces in physical environments and maintain consistent visual grounding throughout multi-step reasoning. However, current vision-language models rely on text-only or coordinate-augmented chain-of-thought, where entity references remain implicit and ambiguous. This may cause the reasoning process to decouple from visual evidence, entity references to drift across steps, and a causal disconnection between the reasoning trajectory and the final answer, with these problems further amplified in multi-view scenarios due to cross-view appearance changes. To address these issues, we propose Pinned Chain-of-Thought (\pincot{}), a structured reasoning paradigm that pins every reasoning step to visual evidence. \pincot{} introduces the concept of \reasoninganchor{}, which binds each task-relevant entity to a structured visual anchor with entity name, unique identity, view index, and spatial grounding, enabling consistent entity tracking across reasoning steps and views. We build a fully automated data generation pipeline to construct \dataset{}, a high-quality \pincot{}-formatted reasoning dataset. We then train \method{} through three-stage post-training that progressively injects embodied knowledge, structured reasoning ability, and process-supervised alignment, with rewards that directly constrain both anchor localization and identity consistency during reasoning. On 14 benchmarks covering embodied spatial reasoning, multi-view reasoning, and pointing, \method{} with only 4B parameters consistently outperforms 7B level open-source embodied models, achieving a 12\% average improvement over the strongest 7B baseline, Mimo-Embodied. Further analysis shows that \pincot{} improves grounding accuracy and cross-step identity consistency, validating the effectiveness of process supervision.