Problem
Research questions and friction points this paper is trying to address.
Agentic ML Exploration
Ads Ranking
Human ML Iteration
Model Portfolio
Autonomous LLM-agent
Agentic ML Exploration (A-MLE) for Ads Ranking
🤖 AI Summary
本文提出Agentic ML Exploration (A-MLE)系统，通过自动化探索机器学习技术来加速广告排名模型的迭代过程，解决人力瓶颈问题。
📝 Abstract
Modern industrial ads ranking stacks are increasingly bottlenecked not by model capacity or training compute, but by the throughput of human ML iteration - the cycles of research, implementation, training, debugging, evaluation, and launch required to surface a single statistically significant improvement. A typical ranking stack contains numerous differentiated models with heterogeneous data, architectures, and infrastructure constraints, and each cycle takes days to weeks of senior engineer attention per model. As a result, techniques that have proven effective on one model diffuse into others slowly and unevenly, leaving substantial recoverable signal unexplored. We present Agentic ML Exploration (A-MLE), an autonomous LLM-agent system that systematically explores ML techniques across a portfolio of ads ranking models. A-MLE decomposes ML iteration into five stages involving hypothesis generation, exploration strategy, experiment execution, result analysis and shared knowledge substrate which are orchestrated by a single agent that invokes domain-specific skills and agentic workflows against a sandboxed execution layer, with human-in-the-loop checkpoints at each stage boundary. We deploy A-MLE across a representative set of large-scale ads ranking models and evaluate it along a tiered capability framework (tool availability, autonomous workflow execution, and open-ended exploration). We further report a controlled cross-LLM study using a fixed agent loop, which surfaces qualitative differences in execution reliability and exploration aggressiveness across the Claude Sonnet, Gemini, and GPT families. We discuss failure modes and the design choices that govern reliability. Our findings suggest that agentic exploration is a practical force multiplier for ML engineers in industrial recommenders, especially for the long tail of models that rarely receive expert attention.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Agentic ML Exploration
autonomous LLM-agent system
ads ranking models
human-in-the-loop checkpoints
cross-LLM study
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