Teaching the Query When to Move: Small Models as Retrieval Controllers
TL;DR for operators A retrieval agent does not merely retrieve documents. It repeatedly decides what to do next: search again, rewrite a query, record evidence, combine what it has found, verify a claim, or stop. The Fellowship of the Query: Learning Retrieval Actions1 shows that these decisions can be taught to small language models as a supervised classification problem. Across the evaluated models, LoRA fine-tuning sharply improves prediction of the next retrieval action. For Granite 4.1 3B, macro-F1 rises from 0.1736 zero-shot to 0.6536 after fine-tuning. ...