How reasoning unlocks parametric knowledge in LLMs

Thinking to recall: How reasoning unlocks parametric knowledge in LLMs

Google Research shows that letting models think before answering sharply improves their ability to recall facts from training data. On Natural Questions, Gemini 2.5 Pro jumps from ~40% accuracy without reasoning to over 70% with it. The gain comes from the model connecting fuzzy memories into verifiable chains, not from external retrieval. The reasoning traces often include self-questioning and fact-checking steps. The post only covers QA tasks so far.

Why it matters: Google Research published a mechanism study with concrete numbers showing how reasoning helps models retrieve parametric knowledge, with a clear 40%→70% jump. Missing generalization evidence beyond Natural Questions keeps the score from going higher. Useful for RAG and eval pr...