← All IntelIntel / article 
Agentic Data Engineering for LLMs: System Design and a Controlled Empirical Study
- Source
- arxiv.org
- Author
- Ruomeng Ding, Qianli Shen, Zhaoyang Han, Meixin Chen, Daoyuan Chen, Yaliang Li
- Date

Why it matters
Agents that use cumulative experiment feedback found data and reward strategies that beat generic baselines on held-out benchmarks. This is a concrete pattern for automating training-data engineering.
Terms in this piece · Glossary
- LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
Read the source arxiv.org
Recommended reads
articleDeepInstructor: An Agentic AI Instructor for Experience-Driven Idea EvaluationRongcan Pei, Fang Guo, Qinglin Qi, Qi Zhu, Yun Luo, Jianhao Yan, Minjun Zhu, Qiujie Xie, Dehong Zheng, Yue Zhang
articleLabAgent: Customize Any Research Hubs for Scientific Discoveries Using AI AgentsLei Liu, Yikun Zhang, Jialin Chen, Wanjia Zhao, Rex Ying, Wengong Jin, Hua Xu, James Zou, Tianyu Liu, Hongyu Zhao
articleBacktrader-Bench: Benchmarking LLM Agents on Algorithmic Trading with Self-Generated MCQsRuoxi Zhao, Maziar Raissi
Comments
Checking sign-in…
Loading comments…