InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to
  Deliberative Reasoners
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.
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Yuhang Liu
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Pengxiang Li
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2025-04-22T09:51:50.804Z
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Pengxiang Li
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Congkai Xie
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Congkai Xie
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Xavier Hu
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Xavier Hu
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Xiaotian Han
claimed_verified
2025-04-22T09:51:27.543Z
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Xiaotian Han
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Shengyu Zhang
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Hongxia Yang
6806e9e819a9fa609609fe6c
Fei Wu
InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to
  Deliberative Reasoners
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.
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.
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