Meta's CORAL: An LLM-Native Harness That Continuously Optimizes Production Recommenders
_reachsumit · x · 2026-09-03
Meta introduces CORAL (Constraint-Optimized Recommender via an Agentic Loop), an LLM-native harness that closes a continual optimization loop around live recommender systems.
- Motivation: Production recommenders shape content for billions, but tuning retrieval, ranking, and serving configs still relies on slow, reactive human-driven online experiments.
- How it works: Each cycle, the agent observes operating signals, reasons over a memory of past decisions and outcomes, and invokes tools — including a numerical optimizer that keeps changes within a fixed operating budget — to reconfigure the recommender; measured results feed the next cycle.
- Formulation: A partially observed, non-stationary, constrained optimization problem where the policy improves in context from its own prior actions, without parameter updates.
- Validation: A/B tested on two large-scale social platforms, lifting engagement and efficiency.
Related event: Meta Unveils CORAL: Agentic Loop Powering Production Recommenders(2 posts)→
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