Recent advances in deep thinking models have demonstrated remarkable reasoning capabilities on mathematical and coding tasks. However, their effectiveness in embodied domains which require continuous interaction with environments through image action interleaved trajectories remains largely -unexplored. We present Embodied Reasoner, a model that extends o1 style reasoning to interactive embodied search tasks. Unlike mathematical reasoning that relies primarily on logical deduction, embodied scenarios demand spatial understanding, temporal reasoning, and ongoing self-reflection based on interaction history. To address these challenges, we synthesize 9.3k coherent Observation-Thought-Action trajectories containing 64k interactive images and 90k diverse thinking processes (analysis, spatial reasoning, reflection, planning, and verification). We develop a three-stage training pipeline that progressively enhances the model's capabilities through imitation learning, self-exploration via rejection sampling, and self-correction through reflection tuning. The evaluation shows that our model significantly outperforms those advanced visual reasoning models, e.g., it exceeds OpenAI o1, o3-mini, and Claude-3.7 by +9\%, 24\%, and +13\%. Analysis reveals our model exhibits fewer repeated searches and logical inconsistencies, with particular advantages in complex long-horizon tasks. Real-world environments also show our superiority while exhibiting fewer repeated searches and logical inconsistency cases.
View on arXiv@article{zhang2025_2503.21696,
title={ Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks },
author={ Wenqi Zhang and Mengna Wang and Gangao Liu and Xu Huixin and Yiwei Jiang and Yongliang Shen and Guiyang Hou and Zhe Zheng and Hang Zhang and Xin Li and Weiming Lu and Peng Li and Yueting Zhuang },
journal={arXiv preprint arXiv:2503.21696},
year={ 2025 }
}