What Makes Good Agentic Data? An ACE Lens on Data Generation for LLM Agents
LLM agents increasingly rely on generated interaction data to learn how to interact with external environments. Agentic data generation must maintain consistency among environments, tasks, interactions, and success signals while producing experience that is useful rather than merely abundant. Existing work spans many agent domains, but domain-centered organization and heterogeneous evaluation often obscure common generation mechanisms and conflate candidate construction with verification and selection. This work develops a two-level framework for the field. First, we represent agentic data as
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzySimilar title/name (fuzzy) · 87%NirDiamant/GenAI_Agents →
“Fuzzy title match (0.94): “What Makes Good Agentic Data? An ACE Lens on Data Generation” ≈ “NirDiamant/GenAI_Agents””
- FuzzySimilar title/name (fuzzy) · 84%Unity-Technologies/ml-agents →
“Fuzzy title match (0.92): “What Makes Good Agentic Data? An ACE Lens on Data Generation” ≈ “Unity-Technologies/ml-agents””
- FuzzyOverlapping authors or contributors · 62%affaan-m/ECC →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%hiyouga/LlamaFactory →
“Shared author/contributor keys: lin”
- LinkedLinked via arxiv author · 85%Xingshan Zeng →
“What Makes Good Agentic Data? An ACE Lens on Data Generation for LLM Agents”
- LinkedLinked via arxiv author · 85%Zishan Xu →
“What Makes Good Agentic Data? An ACE Lens on Data Generation for LLM Agents”
- LinkedLinked via arxiv author · 85%Boju Zhang →
“What Makes Good Agentic Data? An ACE Lens on Data Generation for LLM Agents”