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Meta’s Ranking Engineer Agent (REA) autonomously executes key steps across the end-to-end machine learning (ML) lifecycle for ads ranking models.

This post covers REA’s ML experimentation capabilities: autonomously generating hypotheses, launching training jobs, debugging failures, and iterating on results. Future posts will cover additional REA capabilities.

REA reduces the need for manual intervention. It manages asynchronous workflows spanning days to weeks through a hibernate-and-wake mechanism, with human oversight at key strategic decision points.

In its first production rollout, REA delivered: 2x Model Accuracy: REA-driven iterations doubled average model accuracy over baseline across six models.…

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