📄 GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL
👥 Authors: Rui Yang, Qianhui Wu, Zhaoyang Wang, Hanyang Chen, Ke Yang et al.
📅 Published: 2026-02-25
🔥 Upvotes: 4
🎯 What This Research Is About
Open-source native GUI agents have been struggling to keep up with their closed-source counterparts, especially when it comes to long-horizon navigation tasks. GUI-Libra addresses this gap by introducing a novel training framework that combines two key innovations:
The researchers identified that standard supervised fine-tuning (SFT) with chain-of-thought reasoning often hurts the agent's ability to ground actions properly. Additionally, step-wise reinforcement learning faces challenges with partial verifiability - where multiple actions might be possible, but some are better than others.
💡 Why This Matters
- Bridging the Gap: This work directly tackles the performance gap between open-source and closed-source GUI agents, making advanced AI assistance more accessible to everyone.
- Better Training Methods: By introducing action-aware supervision and partially verifiable reinforcement learning, GUI-Libra shows how to train agents that can both reason effectively and take accurate actions in graphical user interfaces.
- Long-Horizon Tasks: The framework specifically improves performance on complex, multi-step navigation tasks - the kind of real-world scenarios where GUI agents are most useful but traditionally struggle.
- Open-Source Advancement: This research strengthens the open-source AI ecosystem by providing techniques that help community models compete with proprietary systems.
🔍 Key Innovation
GUI-Libra introduces a two-pronged approach: action-aware supervision that ensures reasoning aligns with actual UI actions, and partially verifiable RL that can learn from scenarios where multiple valid actions exist but some are more optimal than others.
Curated from Hugging Face daily papers by AMS IT Services AI Research Team