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Routed Continual Learning (Routed CL) is a continual learning architecture that decides how to partition and retain experience, with its core idea being to treat continual learning as a routing problem—selecting not only the learning mechanism but also the state cluster, rather than committing globally to a single representation.
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10/30/2026
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UnverifiedRouted CL groups past trajectories into multiple clusters, each maintained by one memory system from a fixed library of memory systems; before each run, an LLM-based router decides, based on the problem statement, memory system descriptions, and cluster summaries, whether to reuse an existing cluster or create a new one.70% similarUnverifiedContextual bandit is a class of sequential decision-making frameworks in reinforcement learning: the agent observes contextual features each round, selects an action and receives only an immediate reward, without involving the step-by-step credit assignment problem and without requiring temporal backup estimation of a value function.59% similarUnverifiedRouting collapse is a classic challenge in training mixture-of-experts and dynamic routing architectures, where routers easily fall into local optima causing all tokens to be assigned to the same path.57% similarUnverifiedIn the context of reinforcement learning, a rollout refers to the process of an agent completing one full pass through an environment or policy to generate a single trajectory.57% similarUnverifiedHow to automatically expand the memory system library and how to handle transfer learning between clusters remain directions worth exploring in the future.57% similar
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