Ao Qu and collaborators open-source Reef for learning from live agent interactions
The MIT researcher and a distributed contributor group built an Apache-2.0 stack that updates model weights and agent harnesses from live experience.
By Ryan Merket · Published
Primary source: Hugging Face Newsroom
Why it matters
Reef turns inference logs into a versioned learning pipeline for both model weights and agent software. Its release controls evaluate candidate updates before publication and leave the serving version unchanged when a candidate is rejected.
MIT researcher Ao Qu and a distributed group of AI researchers open-sourced Reef on September 15th, putting agent learning directly inside the infrastructure that serves user requests.
The project describes Reef as fully open source.…
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