Computational Efficiency for Lifelong LLM Agents

Establish computationally efficient mechanisms for truly lifelong large language model agents that continuously distill, deduplicate, integrate, score, retrieve, and prune an expanding experience base of strategic principles, ensuring scalable memory management and inference-time utilization without performance degradation as the experience base grows over extended deployments.

Background

EvolveR introduces a closed-loop experience lifecycle in which an agent collects trajectories during online interaction, distills them into abstract strategic principles during offline self-distillation, and maintains a curated experience base with semantic deduplication and dynamic scoring. Over time, this experience base grows as the agent accumulates more interactions and distilled knowledge.

While the paper proposes curation operations such as deduplication, integration, and periodic pruning based on metric scores to manage growth, the authors explicitly note that achieving computational efficiency at the scale required for truly lifelong learning agents remains unresolved. This highlights the need for methods that keep storage, retrieval, and policy updates efficient even as the experience base becomes large and long-lived.