She was added to a Slack channel, where it was clear she was entering a project already underway. Hundreds of people were busy writing examples of prompts someone might ask a chatbot, writing the chatbot’s ideal response to those prompts, then creating a detailed checklist of criteria that defined that ideal response. Each task took several hours to complete before the data was sent to workers stationed somewhere down the digital assembly line for further review. Katya wasn’t told whose AI she was training — managers referred to it only as “the client” — or what purpose the project served. But she enjoyed the work. She was having fun playing with the models, and the pay was very good. “It was like having a real job,” she says.
In addition, we trained Phi-4-reasoning-vision-15B to have skills that can enable agents to interact with graphical user interfaces by interpreting screen content and selecting actions. With strong high-resolution perception and fine-grained grounding capabilities, Phi-4-reasoning-vision-15B is a compelling option as a base-model for training agentic models such as ones that navigate desktop, web, and mobile interfaces by identifying and localizing interactive elements such as buttons, menus, and text fields. Due to its low inference-time needs it is great for interactive environments where low latency and compact model size are essential.
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