Memory Optimization

Train agents to efficiently store, retrieve, and update information across long interactions using learned memory mechanisms — including RAG fine-tuning, memory consolidation, and context compression. Memory optimization trains agents to manage their own context window: deciding what to remember, what to forget, and how to retrieve relevant information when needed. Key techniques include fine-tuning for retrieval-augmented generation (RAG) where the model learns to condition on retrieved documents effectively; memory consolidation where short-term memories are summarized into long-term storage; and context compression where long histories are distilled into compact representations.