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Online Learning for Agents(OLA)
Incrementally updating a model from a data stream while controlling drift, feedback loops, and rollback risk
In 30 seconds
- What
- Incrementally retrains a model on streaming labeled data while detecting concept drift, versioning updates, and rolling back if performance degrades.
- When to use
- Production classifiers or rankers receiving continuous feedback where retraining on all historical data is infeasible or unsafe.
- Watch out
- Delayed or out-of-order labels corrupt the training signal; validate event order and handle label corrections explicitly before updating.
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Online Learning for Agents: Overview
Incrementally updating a model from a data stream while controlling drift, feedback loops, and rollback risk
- Prequential evaluation
- Incremental model updates
- Concept-drift detection
- Bounded replay or rolling windows
- Shadow and canary deployment
- Versioning and rollback
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References
The papers, specifications, and repositories this pattern is based on.
- Learning under Concept Drift: A Review (2020)arXiv:2004.05785
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