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The training data for on-policy distillation is generated by the interaction of the student's current policy with the environment, and the state distribution changes as the student policy is updated, in line with the DAgger idea, which can mitigate distribution shift.
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10/22/2026
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UnverifiedOnline adaptation methods leverage meta-learning or rapid fine-tuning mechanisms to enable policies to dynamically adjust based on real-environment feedback from the early stages of deployment.78% similarVerifiedOnline policy distillation (OPD) has the student model generate its own training responses, which are then supervised by the teacher model.78% similarUnverifiedBehavior cloning transforms policy learning into a supervised learning problem, using environment states as inputs and expert actions as labels to train a mapping function from states to actions.76% similarUnverifiedMitigation paths proposed by researchers and in community discussions include: real-time detection at the output end, continuous iteration of adversarial training, user behavior analysis, and industry information sharing.75% similarUnverifiedThe training data for on-policy distillation is generated by real-time sampling of the student model under its current parameters, rather than being a pre-prepared fixed corpus, which contrasts with offline distillation.74% similar
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