BALANCE: Benchmarking Continual Learning under Changing Deployment Budgets

Continual learners adapt to changing tasks under deployment budgets that can vary independently. We introduce Benchmark for Adaptive Lifelonglearning with Network Control and Elasticity (BALANCE) for studying model lifecycle control under these joint changes. External requests reclaim capacity at announced deadlines and return it later. A controller schedules resizing, candidate training and deployment while the current learner serves the task stream. Preparation and updates consume interaction time; ready deployment is instantaneous. Two recurrent prediction families combine locally shared task structure with limited deployment capacity: shared linear rules support analytical capacity calibration, and shared nonlinear features support evaluation with ordinary task-conditioned MLPs. We develop a resource- and deadline-aware model-maintenance algorithm whose rule-based implementations provide reference baselines in BALANCE. An accompanying feasibility study uses imitation-initialized reinforcement learning to learn lifecycle decisions under the same resource protocol. Paired experiments show that maintenance cadence changes the trade-off between prediction performance and model-maintenance work at similar average deployed sizes. A controlled throughput-shift study in the linear family finds that sensitivity to maintenance speed accompanies increased candidate cancellation and reduced deployed capacity despite resource compliance.