title: Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for
    Embodied Interactive Tasks
summary: 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.
paper_authors._id: 67e64ff17fe72aad5c26ab2f
paper_authors.name: Wenqi Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T09:10:00.794Z
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paper_authors.avatarUrl: /avatars/1427cf1a72b5db0cb263ad45885cf925.svg
paper_authors.fullname: Wenqi Zhang
paper_authors.type: user
paper_authors.user: zwq2018
paper_authors._id: 67e64ff17fe72aad5c26ab30
paper_authors.name: Mengna Wang
paper_authors._id: 67e64ff17fe72aad5c26ab31
paper_authors.name: Gangao Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T09:09:24.111Z
paper_authors._id: 64bb986ac733e8552fa4c5d1
paper_authors.avatarUrl: /avatars/78dc5f802def0bdebd890438fcb1f966.svg
paper_authors.fullname: Gangao Liu
paper_authors.type: user
paper_authors.user: Gangao
paper_authors._id: 67e64ff17fe72aad5c26ab32
paper_authors.name: Xu Huixin
paper_authors._id: 67e64ff17fe72aad5c26ab33
paper_authors.name: Yiwei Jiang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T09:09:05.352Z
paper_authors._id: 60abbe1fe3de7c7440abb84d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1621868056572-noauth.jpeg
paper_authors.fullname: Yiwei Jiang
paper_authors.type: user
paper_authors.user: yijiang
paper_authors._id: 67e64ff17fe72aad5c26ab34
paper_authors.name: Yongliang Shen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T09:08:58.629Z
paper_authors._id: 5e1058e9fcf41d740b69966d
paper_authors.avatarUrl: /avatars/ce74839ba871f2b54313a670a233ba82.svg
paper_authors.fullname: Yongliang Shen
paper_authors.type: user
paper_authors.user: tricktreat
paper_authors._id: 67e64ff17fe72aad5c26ab35
paper_authors.name: Guiyang Hou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T09:08:43.400Z
paper_authors._id: 67c03110e8c7d56a8e135ac8
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/eP3y_8_tyB8tcrT7py4L7.png
paper_authors.fullname: Hou
paper_authors.type: user
paper_authors.user: Guiyang1001
paper_authors._id: 67e64ff17fe72aad5c26ab36
paper_authors.name: Zhe Zheng
paper_authors._id: 67e64ff17fe72aad5c26ab37
paper_authors.name: Hang Zhang
paper_authors._id: 67e64ff17fe72aad5c26ab38
paper_authors.name: Xin Li
paper_authors._id: 67e64ff17fe72aad5c26ab39
paper_authors.name: Weiming Lu
paper_authors._id: 67e64ff17fe72aad5c26ab3a
paper_authors.name: Peng Li
paper_authors._id: 67e64ff17fe72aad5c26ab3b
paper_authors.name: Yueting Zhuang
paper_title: Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for
    Embodied Interactive Tasks
paper_summary: 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.
paper_ai_summary: Embodied Reasoner, a deep learning model for interactive embodied tasks, outperforms existing visual reasoning models by leveraging observation-thought-action trajectories and a training pipeline that includes imitation learning, exploration, and reflection.
paper_ai_keywords: Embodied Reasoner observation-thought-action trajectories imitation learning rejection sampling reflection tuning visual reasoning models OpenAI o1 OpenAI o3-mini Claude-3.7