The provided sources collectively introduce CARTRIDGES, a novel paradigm for enhancing Large Language Model (LLM) efficiency when handling large, repeatedly accessed text corpora. CARTRIDGES function as optimized, smaller Key-Value (KV) caches trained offline using a method called SELF-STUDY, which involves generating synthetic conversational data and applying a context-distillation objective. This approach significantly reduces memory consumption and increases throughput compared to traditional in-context learning (ICL), while maintaining or even improving response quality and extending effective context length. Furthermore, CARTRIDGES are shown to be composable, allowing multiple document representations to be combined for multi-document querying without retraining. This innovation addresses the high computational cost of ICL, making long-context LLM applications more feasible.