Recent autonomous machine learning engineering (MLE) agents have made significant progress on public leaderboards. Often motivated by progress stagnation over long-horizon cycles and limited Large Language Model (LLM) primitives, modern MLE agents are deployed on top of increasingly elaborate machinery: multi-agent orchestrators, dedicated retrieval subagents, and more. While such harnesses expand, the use of more primitive but improved coding agents—where LLMs have direct access to the execution environment through read, write, and bash primitives—has received little attention in the field…
Read the original at Apple machine learning research: How Much of a Harness Does a Strong Agent Need for Autonomous ML Engineering?
Source: https://machinelearning.apple.com/research/harness-autonomous-ml-engineering