UI-AGILE
GUI agent trained by RL with a continuous reward, a simple-thinking reward and cropping-based resampling on about 9k samples, plus a plug-in decomposed grounding-and-selection inference method.
AI-assisted editorial review against a primary source. This is a catalog annotation, not an independent reproduction or endorsement.
What is this resource?
- Resource type
- Models and checkpoints
- Environment
- cross-platform
- Topics
- grounding, reinforcement-learning
- Record publication date
- Not recorded
- Evidence retrieved
- 2026-10-16T06:36:00Z
- Resource ID
- github:kdegroup/ui-agile
What licenses are recorded?
Code, training data, weights, and documents can have different terms. A blank declaration means unknown, not unrestricted use.
- Code
- MIT (repository LICENSE header; not a weights/data license)
- Data
- Not recorded
- Document
- Not recorded
- Weights
- Not recorded
Explore related topics
Related resources
- documented by: UI-AGILE: Advancing GUI Agents with Effective Reinforcement Learning and Precise Inference-Time Grounding
Primary evidence and verification
Source retrieval is separate from experiment reproduction. Use the original publication or publisher page when citing claims about the resource.
- editorial primary review
Retrieved 2026-10-16T06:36:00Z. Supports: name, kind, summary, environments, topics, scope, relations.
Content fingerprint
eb2a2767741b53e5a6659ef4b0cc325ac18ffdc4222deef1d0e7671089fcaa39 - license header
Retrieved 2026-10-16T06:42:01Z. Supports: licenses.code.
Content fingerprint
a145c4ec8fd43f59ebe97708b6ee4cef8112959036957eaa4739b8da9a297e8a
Suggest a correction
Open a source-backed correction. Include this record ID and the original source. Read our editorial policy and coverage boundaries.