The Sequence Knowledge #772: Generate Data Using Multiturn Data Synthesis
A more sophisticated synthetic data generation paradigm.
Today we will Discuss:
An introduction to multiturn data synthesis for data generation.
A review of the famous Reflexion paper that uses synthetic data to improve AI agents.
💡 AI Concept of the Day: What is Multiturn Data Synthesis?
Multi-turn synthesis and self-play are other important categories in synthetic data generation . These methods treat data generation as an interactive process rather than a single shot. Instead of asking a model to answer once, we let agents act, react, and revise—with tools or against each other—so the dataset captures plans, errors, fixes, and decisions. That structure is exactly what smaller students need to learn capabilities like tool use, coding, browsing, negotiation, and safety. The result isn’t just more data; it’s richer supervision: dialogues, traces, edit sequences, rewards, and verifier outcomes that teach how to get to an answer, not only what the answer is.