Extending RCL to Continual Deployment with Non-Stationary Task Distributions

Extend Reflective Context Learning (RCL) to continual deployment settings in which the task distribution shifts over time, and construct mechanisms that enable the playbook to adapt to new conditions while retaining previously learned behaviors to avoid catastrophic forgetting.

Background

The study evaluates RCL in fixed benchmark settings and analyzes optimization primitives for stability, variance reduction, and forgetting. While replay buffers and optimizer state mitigate forgetting during offline-style training, deployment settings introduce non-stationarity and shifting task distributions.

The authors explicitly flag the extension of RCL to continual deployment as an open direction, emphasizing the need to adapt without forgetting as tasks evolve over time.