research
Agent Data Needed More Than Volume
The ACE framework separates grounded accuracy, learner-relative complexity and behavioral diversity in generated agent experience.
Summary
The ACE framework separates grounded accuracy, learner-relative complexity and behavioral diversity in generated agent experience.
The survey represents agent data as environment, task, interaction and optional verifier, then treats generation as constrained distribution design. Its synthesis finds a shift toward execution-grounded validity, difficulty calibrated to a declared learner and diversity beyond surface variation. This is a conceptual map of prior work, not a new empirical dataset.
Why it matters
The ACE framework separates grounded accuracy, learner-relative complexity and behavioral diversity in generated agent experience.
Limits and context
- This is a conceptual map of prior work, not a new empirical dataset.
Key claims
The ACE framework separates grounded accuracy, learner-relative complexity and behavioral diversity in generated agent experience.
Qualification: This is a conceptual map of prior work, not a new empirical dataset.
Evidence: source-2026-08-28-012
Sources
- arXiv preprint 2608.27260arXiv · primary research
Corrections
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