Research
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September 2026
Rethinking OPD II
Rethinking On-Policy Distillation of Large Language Models II: One Training Example
Examines the role of training data in on-policy distillation at the data-minimal limit, training on a single query. One-shot OPD keeps improving for hundreds of steps and recovers most of full-data OPD's gain across task domains and model families. A single query reaches 71.5% state coverage relative to full-data OPD and sixteen reach 98.9%, matching full-data training — OPD is therefore data-overfed but algorithm-starved.
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April 2026
Rethinking OPD
Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe
A systematic investigation of on-policy distillation dynamics. Identifies the two conditions that govern whether OPD succeeds — compatible thinking spaces between student and teacher, and genuinely new teacher capability — traces the token-level mechanism behind them, and derives practical recipes for recovering failing runs.