AutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration - Learning from Cheap, Optimizing Expensive

In three linesAutoLLMResearch introduces an agentic framework to automate configuration of expensive LLM experiments. The system learns from low-fidelity experiments to extrapolate toward promising high-fidelity configurations. LLMConfig-Gym provides a multi-fidelity environment with >1M GPU hours of verified experiment outcomes.

Summary generated by Claude — human-verified