Synthesizing Agentic Factual SFT/Mid‑train Data: Query Filtering, Trajectory Generation, and Tool Usage

The article outlines a practical pipeline for creating agentic factual SFT and mid‑train datasets, covering how to define training goals, filter and classify queries, label processing tags, format trajectory samples, differentiate SFT from mid‑train data, and avoid common pitfalls when generating evidence‑driven AI training data.

SFTagentic AIdata synthesis

0 likes · 10 min read

Synthesizing Agentic Factual SFT/Mid‑train Data: Query Filtering, Trajectory Generation, and Tool Usage

Machine Learning Algorithms & Natural Language Processing

May 17, 2026 · Artificial Intelligence

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.

SFTagentic AIdata synthesis

0 likes · 11 min read

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