content.raw: 🚀 DeepSeek R1 moment has come for GUI agents: Rule-based Reinforcement Learning gives better results than SFT with 500x smaller datasets!
content.type: text
content.value: 🚀 DeepSeek R1 moment has come for GUI agents: Rule-based Reinforcement Learning gives better results than SFT with 500x smaller datasets!
content.type: new_line
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content.raw: Traditionally (by which I mean "in the last few months"), GUI agents have been trained with supervised fine-tuning (SFT). This meant, collecting huge datasets of screen captures from people using computers, and using these to fine-tune your model. 📚
content.type: text
content.value: Traditionally (by which I mean "in the last few months"), GUI agents have been trained with supervised fine-tuning (SFT). This meant, collecting huge datasets of screen captures from people using computers, and using these to fine-tune your model. 📚
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content.raw: 👉 But last week, a new paper introduced UI-R1, applying DeepSeek's R1-style rule-based reinforcement learning (RL) specifically to GUI action prediction tasks. 
content.type: text
content.value: 👉 But last week, a new paper introduced UI-R1, applying DeepSeek's R1-style rule-based reinforcement learning (RL) specifically to GUI action prediction tasks. 
content.type: new_line
content.raw: This is big news: with RL, maybe we could build good agents without the need for huge datasets.
content.type: text
content.value: This is big news: with RL, maybe we could build good agents without the need for huge datasets.
content.type: new_line
content.type: new_line
content.raw: UI-R1 uses a unified reward function that evaluates multiple responses from models, optimizing via policy algorithms like Group Relative Policy Optimization (GRPO).
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content.value: UI-R1 uses a unified reward function that evaluates multiple responses from models, optimizing via policy algorithms like Group Relative Policy Optimization (GRPO).
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content.raw: Specifically, the reward function assesses:
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content.value: Specifically, the reward function assesses:
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content.raw: 🎯 Action type accuracy: Does the predicted action match the ground truth?
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content.value: 🎯 Action type accuracy: Does the predicted action match the ground truth?
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content.raw: 📍 Coordinate accuracy (specifically for clicks): Is the predicted click within the correct bounding box?
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content.value: 📍 Coordinate accuracy (specifically for clicks): Is the predicted click within the correct bounding box?
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content.raw: 📑 Output format: Does the model clearly articulate both its reasoning and final action?
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content.value: 📑 Output format: Does the model clearly articulate both its reasoning and final action?
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content.raw: Using just 136 carefully selected mobile tasks—compared to 76,000 tasks for larger models like OS-Atlas—UI-R1 shows significant efficiency and improved performance:
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content.value: Using just 136 carefully selected mobile tasks—compared to 76,000 tasks for larger models like OS-Atlas—UI-R1 shows significant efficiency and improved performance:
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content.raw: 📈 Boosted action prediction accuracy from 76% to 89% on AndroidControl.
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content.value: 📈 Boosted action prediction accuracy from 76% to 89% on AndroidControl.
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content.raw: 🌐 Outperformed larger, SFT-trained models (e.g., OS-Atlas-7B), demonstrating superior results with vastly fewer data points (136 tasks vs. 76K).
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content.value: 🌐 Outperformed larger, SFT-trained models (e.g., OS-Atlas-7B), demonstrating superior results with vastly fewer data points (136 tasks vs. 76K).
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content.raw: 🔍 Enhanced adaptability and generalization, excelling even in out-of-domain scenarios.
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content.value: 🔍 Enhanced adaptability and generalization, excelling even in out-of-domain scenarios.
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content.raw: The paper tests this RL-based method only in low-level GUI tasks. Could it generalize to more complex interactions? 🧐
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content.value: The paper tests this RL-based method only in low-level GUI tasks. Could it generalize to more complex interactions? 🧐
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content.raw: Read the full paper here 👉 
content.type: text
content.value: Read the full paper here 👉 
content.label: UI-R1: Enhancing Action Prediction of GUI Agents by Reinforcement
    Learning (2503.21620)
content.raw: https://huggingface.co/papers/2503.21620
content.id: 2503.21620
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2503.21620
rawContent: 🚀 DeepSeek R1 moment has come for GUI agents: Rule-based Reinforcement Learning gives better results than SFT with 500x smaller datasets!
  
  Traditionally (by which I mean "in the last few months"), GUI agents have been trained with supervised fine-tuning (SFT). This meant, collecting huge datasets of screen captures from people using computers, and using these to fine-tune your model. 📚
  
  👉 But last week, a new paper introduced UI-R1, applying DeepSeek's R1-style rule-based reinforcement learning (RL) specifically to GUI action prediction tasks. 
  This is big news: with RL, maybe we could build good agents without the need for huge datasets.
  
  UI-R1 uses a unified reward function that evaluates multiple responses from models, optimizing via policy algorithms like Group Relative Policy Optimization (GRPO).
  
  Specifically, the reward function assesses:
  🎯 Action type accuracy: Does the predicted action match the ground truth?
  📍 Coordinate accuracy (specifically for clicks): Is the predicted click within the correct bounding box?
  📑 Output format: Does the model clearly articulate both its reasoning and final action?
  
  Using just 136 carefully selected mobile tasks—compared to 76,000 tasks for larger models like OS-Atlas—UI-R1 shows significant efficiency and improved performance:
  📈 Boosted action prediction accuracy from 76% to 89% on AndroidControl.
  🌐 Outperformed larger, SFT-trained models (e.g., OS-Atlas-7B), demonstrating superior results with vastly fewer data points (136 tasks vs. 76K).
  🔍 Enhanced adaptability and generalization, excelling even in out-of-domain scenarios.
  
  The paper tests this RL-based method only in low-level GUI tasks. Could it generalize to more complex interactions? 🧐
  
  Read the full paper here 👉 https://huggingface.co/papers/2503.21620
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