WDL-OPD: Weak-Driven On-Policy Distillation via Mixture-Constrained Co-Training
On-policy distillation (OPD) aligns a student with a teacher on trajectories sampled from the student itself, reducing the train-test state mismatch of offline distillation. The same feedback loop can nevertheless be unstable: each update changes both the policy and the states on which the next update is computed. We introduce WDL-OPD, a mixture-constrained co-training method with two trainable policies. An anchor policy generates every rollout, an auxiliary policy evaluates the same visited sta
Record details
Published: 10 August 2026
Source: arXiv
Category: Research
Topics: Regulation · Children & education
Retrieved: 11 August 2026
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ethics.ai (10 August 2026), “WDL-OPD: Weak-Driven On-Policy Distillation via Mixture-Constrained Co-Training,” evidence record 18010, https://ethics.ai/record/18010 (originally published by arXiv).
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