Instruction Retrieval at Inference Time for Small Language Models
The study proposes using instruction retrieval to boost small language model reasoning without retraining or scaling. It builds a corpus of structured reasoning procedures by clustering training data and generating reusable guides. At inference, it retrieves the most relevant instructions based on similarity to the query, providing necessary background and steps for complex tasks.