rag-retro-25x-smaller

Summary

You don't need a model 25 times bigger to capture world knowledge — you can pair a much smaller network with a retrieval step that pulls in relevant facts on the fly, and it performs just as well. This implies that scaling up model size is not the only (or best) path to better performance; architecture and external memory can substitute for brute-force parameter growth, which has real implications for cost, deployment size, and where the system stores its knowledge.