Teaching Agents to Ask Effective Clarification Questions

Humans do not always express what they need perfectly. Helpful assistants must be able to ask for clarification to handle real-world user commands which may be underspecified or poorly stated. Yet, optimal clarification remains challenging. The space of possible tasks is large, and not all missing information is equally valuable. We study real software engineering tasks. These tasks require the user to provide many details, some of which are more important than others, and an accurate assistant should be able to determine which clarification questions to ask when necessary information is missing. We systematically quantify which types of information in prompts types most impact task success and what types of clarification questions enable productive answers by simulated users. Using Shapley attribution and distributional comparisons, we identify two learnable properties of effective clarification: task relevance (which information impacts success) and user answerability (what users can realistically provide). We operationalize these as multi-stage reinforcement learning rewards to train an 8B parameter module matching GPT-5's performance while generating 41\% fewer questions. Our work demonstrates that effective clarification emerges from grounding reward design in empirical analysis of information impact and user answerability, establishing a generalizable reward framework applicable across interactive task domains.