Baseten Training: an autoresearch substrate

This post introduces the concept of an 'autoresearch substrate' built on Baseten Training, formalizing the loop of human-defined research programs executed by AI agents. It details how Baseten Training's features (CLI-first, containerization, on-demand compute, observable results, single entrypoint submission) naturally support this agentic iteration, avoiding common pitfalls of unreproducible experiments. The post provides concrete examples of prompts and experimental outcomes, highlighting how this setup accelerates methodical, bounded optimization within a defined search space, shifting the researcher's role to curating the search space rather than running experiments manually. It also discusses the limitations of pure agentic iteration and the need for human intervention to drive novelty.