How to Build Agentic Factual SFT and Mid‑Train Datasets: Query Selection, Trajectory Generation, and Tool Usage

This article outlines a systematic approach for creating agentic factual SFT and Mid‑train data, covering the definition of training goals, query filtering, two‑layer classification and labeling, trajectory format, differences between Mid‑train and SFT, a practical synthesis pipeline, and common pitfalls to avoid.

Agentic AISFTdata synthesis

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How to Build Agentic Factual SFT and Mid‑Train Datasets: Query Selection, Trajectory Generation, and Tool Usage

JD Tech Talk

Oct 12, 2020 · Artificial Intelligence

Transfer Learning for Human Mobility Modeling in New Cities

The paper presented at WWW 2020 proposes a transfer‑learning framework that leverages POI, road‑network and traffic data from existing cities to generate realistic human mobility trajectories for a target city by modeling mobility intentions, origin‑destination pairs, and routes, and validates the approach with extensive experiments across multiple Chinese cities.

AIUrban Computingdomain generalization

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Transfer Learning for Human Mobility Modeling in New Cities