🚀 DeepSeek R1 moment has come for GUI agents: Rule-based Reinforcement Learning gives better results than SFT with 500x smaller datasets!
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🚀 DeepSeek R1 moment has come for GUI agents: Rule-based Reinforcement Learning gives better results than SFT with 500x smaller datasets!
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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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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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👉 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. 
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👉 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. 
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This is big news: with RL, maybe we could build good agents without the need for huge datasets.
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This is big news: with RL, maybe we could build good agents without the need for huge datasets.
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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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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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Specifically, the reward function assesses:
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Specifically, the reward function assesses:
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🎯 Action type accuracy: Does the predicted action match the ground truth?
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🎯 Action type accuracy: Does the predicted action match the ground truth?
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📍 Coordinate accuracy (specifically for clicks): Is the predicted click within the correct bounding box?
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📍 Coordinate accuracy (specifically for clicks): Is the predicted click within the correct bounding box?
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📑 Output format: Does the model clearly articulate both its reasoning and final action?
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📑 Output format: Does the model clearly articulate both its reasoning and final action?
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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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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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📈 Boosted action prediction accuracy from 76% to 89% on AndroidControl.
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📈 Boosted action prediction accuracy from 76% to 89% on AndroidControl.
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🌐 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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🌐 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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🔍 Enhanced adaptability and generalization, excelling even in out-of-domain scenarios.
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🔍 Enhanced adaptability and generalization, excelling even in out-of-domain scenarios.
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The paper tests this RL-based method only in low-level GUI tasks. Could it generalize to more complex interactions? 🧐
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The paper tests this RL-based method only in low-level GUI tasks. Could it generalize to more complex interactions? 🧐
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Read the full paper here 👉 
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Read the full paper here 👉 
UI-R1: Enhancing Action Prediction of GUI Agents by Reinforcement
  Learning (2503.21620)
https://huggingface.co/papers/2503.21620
2503.21620
paper
resource
https://huggingface.co/papers/2503.21620
🚀 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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