LiteGUI: Lightweight GUI Agents via Multi-Solution Guided Distillation and Dual-Level Reinforcement Learning
cs.AI, cs.LG
Submitted: 2026-05-08
Updated: 2026-10-08
Terminology
Sources
- Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
- Qwen3-VL Technical Report
- Qwen2.5-VL Technical Report
- Soft Adaptive Policy Optimization
- Seed1.5-VL Technical Report
- ZonUI-3B: A Lightweight Vision-Language Model for Cross-Resolution GUI Grounding
- GPT-4o System Card
- ShowUI: One Vision-Language-Action Model for GUI Visual Agent
- PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC
- InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners
- Unifying distillation and privileged information
- UI-R1: Enhancing Efficient Action Prediction of GUI Agents by Reinforcement Learning
- GUI-R1 : A Generalist R1-Style Vision-Language Action Model For GUI Agents
- UI-TARS: Pioneering Automated GUI Interaction with Native Agents
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model
- Self-Distillation Enables Continual Learning
- Gemini: A Family of Highly Capable Multimodal Models
- Kimi-VL Technical Report
- OpenCUA: Open Foundations for Computer-Use Agents
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