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Teachers can be coaxed into recovering from their own errors during distillation, unlocking substantial gains in student model performance.
Treating on-policy distillation as value-based RL slashes rollout compute by 75% while actively preserving the generation diversity needed for high-k reasoning.
Adversarial imitation learning can achieve fast $\widetilde{O}(1/\varepsilon)$ sample complexity across both expert demonstrations and online environment interactions—provided you jointly regularize the policy and the reward.