Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization
Teaching AI to remember documents without looking them up during answers
Large language models typically need to retrieve source documents when answering questions about them. Researchers developed a three-stage training method called IAR that lets models internalize document knowledge permanently, so they can answer questions accurately without retrieval — improving domain-specific accuracy by 3.6 percentage points while maintaining general reasoning skills across multiple AI models.
Most AI systems today rely on retrieving relevant documents in real time, which is slow and fails when documents aren't available or are missed by search. A model that permanently learns document content could answer faster and more reliably in settings like customer support, medical records, or legal document systems — where the same documents are queried repeatedly and immediate retrieval isn't always possible.