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Trajectory Sampling

2 articles · Page 1 of 1
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Jun 24, 2026 · Artificial Intelligence

Why Public QA Datasets Fail for Deep Research Agents—and How to Build Effective Training Data

The article explains that single‑ or two‑hop QA datasets cannot teach Deep Research agents multi‑step reasoning, outlines four mainstream data‑construction methods, describes trajectory sampling with a three‑stage funnel filter, and shares practical guidelines on data volume, difficulty distribution, question types, and common pitfalls.

AI Agent TrainingData ConstructionDeep Research
0 likes · 32 min read
Why Public QA Datasets Fail for Deep Research Agents—and How to Build Effective Training Data
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Apr 20, 2026 · Artificial Intelligence

How to Build Multi‑Step Reasoning Training Data for Deep Research Agents

Standard QA datasets fall short for deep research tasks because they lack the multi‑step, dynamic reasoning required; this article explains why, outlines four data‑construction techniques—SailorFog‑QA, WebFrontier, WebShaper, E2HQA—details trajectory sampling, filtering, scale considerations, and interview‑ready explanations.

AI agentsData ConstructionLLM training
0 likes · 16 min read
How to Build Multi‑Step Reasoning Training Data for Deep Research Agents