title: InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to
    Deliberative Reasoners
summary: Multimodal Large Language Models (MLLMs) have powered Graphical User
  Interface (GUI) Agents, showing promise in automating tasks on computing
  devices. Recent works have begun exploring reasoning in GUI tasks with
  encouraging results. However, many current approaches rely on manually designed
  reasoning templates, which may result in reasoning that is not sufficiently
  robust and adaptive for complex GUI environments. Meanwhile, some existing
  agents continue to operate as Reactive Actors, relying primarily on implicit
  reasoning that may lack sufficient depth for GUI tasks demanding planning and
  error recovery. We argue that advancing these agents requires a shift from
  reactive acting towards acting based on deliberate reasoning. To facilitate
  this transformation, we introduce InfiGUI-R1, an MLLM-based GUI agent developed
  through our Actor2Reasoner framework, a reasoning-centric, two-stage training
  approach designed to progressively evolve agents from Reactive Actors to
  Deliberative Reasoners. The first stage, Reasoning Injection, focuses on
  establishing a basic reasoner. We employ Spatial Reasoning Distillation to
  transfer cross-modal spatial reasoning capabilities from teacher models to
  MLLMs through trajectories with explicit reasoning steps, enabling models to
  integrate GUI visual-spatial information with logical reasoning before action
  generation. The second stage, Deliberation Enhancement, refines the basic
  reasoner into a deliberative one using Reinforcement Learning. This stage
  introduces two approaches: Sub-goal Guidance, which rewards models for
  generating accurate intermediate sub-goals, and Error Recovery Scenario
  Construction, which creates failure-and-recovery training scenarios from
  identified prone-to-error steps. Experimental results show InfiGUI-R1 achieves
  strong performance in GUI grounding and trajectory tasks. Resources at
  https://github.com/Reallm-Labs/InfiGUI-R1.
paper_authors._id: 6806e9e819a9fa609609fe65
paper_authors.name: Yuhang Liu
paper_authors._id: 6806e9e819a9fa609609fe66
paper_authors.name: Pengxiang Li
paper_authors.status: claimed_verified
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paper_authors.fullname: Pengxiang Li
paper_authors.type: user
paper_authors.user: pengxiang
paper_authors._id: 6806e9e819a9fa609609fe67
paper_authors.name: Congkai Xie
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-04-22T00:59:21.913Z
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paper_authors.fullname: unk
paper_authors.type: user
paper_authors.user: xieck13
paper_authors._id: 6806e9e819a9fa609609fe68
paper_authors.name: Xavier Hu
paper_authors._id: 6806e9e819a9fa609609fe69
paper_authors.name: Xiaotian Han
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-22T09:51:27.543Z
paper_authors._id: 650dde4ce14eeb01d42b37a1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/650dde4ce14eeb01d42b37a1/n5Yv24uofZ2XJjXdYCrKd.png
paper_authors.fullname: Xiaotian Han
paper_authors.type: user
paper_authors.user: xiaotianhan
paper_authors._id: 6806e9e819a9fa609609fe6a
paper_authors.name: Shengyu Zhang
paper_authors._id: 6806e9e819a9fa609609fe6b
paper_authors.name: Hongxia Yang
paper_authors._id: 6806e9e819a9fa609609fe6c
paper_authors.name: Fei Wu
paper_title: InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to
    Deliberative Reasoners
paper_summary: Multimodal Large Language Models (MLLMs) have powered Graphical User
  Interface (GUI) Agents, showing promise in automating tasks on computing
  devices. Recent works have begun exploring reasoning in GUI tasks with
  encouraging results. However, many current approaches rely on manually designed
  reasoning templates, which may result in reasoning that is not sufficiently
  robust and adaptive for complex GUI environments. Meanwhile, some existing
  agents continue to operate as Reactive Actors, relying primarily on implicit
  reasoning that may lack sufficient depth for GUI tasks demanding planning and
  error recovery. We argue that advancing these agents requires a shift from
  reactive acting towards acting based on deliberate reasoning. To facilitate
  this transformation, we introduce InfiGUI-R1, an MLLM-based GUI agent developed
  through our Actor2Reasoner framework, a reasoning-centric, two-stage training
  approach designed to progressively evolve agents from Reactive Actors to
  Deliberative Reasoners. The first stage, Reasoning Injection, focuses on
  establishing a basic reasoner. We employ Spatial Reasoning Distillation to
  transfer cross-modal spatial reasoning capabilities from teacher models to
  MLLMs through trajectories with explicit reasoning steps, enabling models to
  integrate GUI visual-spatial information with logical reasoning before action
  generation. The second stage, Deliberation Enhancement, refines the basic
  reasoner into a deliberative one using Reinforcement Learning. This stage
  introduces two approaches: Sub-goal Guidance, which rewards models for
  generating accurate intermediate sub-goals, and Error Recovery Scenario
  Construction, which creates failure-and-recovery training scenarios from
  identified prone-to-error steps. Experimental results show InfiGUI-R1 achieves
  strong performance in GUI grounding and trajectory tasks. Resources at
  https://github.com/Reallm-Labs/InfiGUI-R1.
paper_ai_summary: InfiGUI-R1 leverages a reasoning-centric framework to enhance MLLMs for GUI tasks by transitioning them from Reactive Actors to Deliberative Reasoners using Spatial Reasoning Distillation and Reinforcement Learning techniques.
paper_ai_keywords: Multimodal Large Language Models MLLMs Graphical User Interface GUI GUI Agents Reactive Actors Deliberative Reasoners Actor2Reasoner Reasoning Injection Spatial Reasoning Distillation Reinforcement Learning Sub-goal Guidance Error Recovery Scenario Construction GUI grounding trajectory tasks