Meta releases Routed Continual Learning: LLM router partitions agent memory for 15-30% gains
rohanpaul_ai · x · 2026-11-03
Meta's paper "Routed Continual Learning" (arXiv:2610.38796, FAIR; authors include Ilya Mironov) proposes an architecture for deployed agents to learn from past experience at scale.
The problem: a global history preserves every detail but is expensive and mixes unrelated experience; compressing it into one fixed representation saves cost but discards valuable details.
- Routed CL: past trajectories are grouped into clusters, each maintaining state with one memory system from a fixed library; before each run, an LLM-based router reuses an existing cluster or creates a new one based on the problem statement, memory system descriptions, and cluster summaries
- A Context Memory Adapter mediates between the memory system and the agentic loop
On 398 heterogeneous tasks (301 CL-Bench + 97 τ³-Bench banking) across 4 models and 5 matched rollouts, Routed CL raises normalized reward over a single global ICL history by 15-30% of the maximum attainable reward and cuts estimated API cost by 65-72%.
Related event: Meta's Routed Continual Learning Cuts Agent Memory API Costs by Up to 72%(2 posts)→
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