Reproduce “Exploring Autonomous Agentic Data Engineering for Model Specialization” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Yujie Luo, Xiangyuan Ru, Jingsheng Zheng, Jingjing Wang, Yuqi Zhu, Jintian Zhang, Runnan Fang, Kewei Xu and 5 more
Autonomous Agentic Data Engineering frames data curation as an optimization problem in which an LLM plans, codes, validates, and revises a training-data pipeline for a fixed student model. Across science, code, and finance tasks, iterative agents generally outperform one-shot generation, with GPT-5.2 reaching a 57.29% average relative gain from scratch and Claude-4-Sonnet achieving 73.26% with seed data, while failures in filtering, semantic consistency, and distribution balance remain common.
Key insights
- Feedback-driven iteration is more valuable than simply generating a larger batch once: performance gains often occur within the first 8–15 iterations before plateauing, and iterative GPT-5.2 improves substantially over its one-shot counterpart.
- Autonomous refinement primarily broadens instruction and response diversity; improvements in intrinsic solution quality are smaller, indicating that agents are better at expanding coverage than reliably correcting semantic or reasoning errors.
- Seed data acts as a guide rail for weaker agents but does not guarantee better results, since it can constrain exploration and its effect varies by model and domain.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.