Latent On-Policy Self-Distillation for End-to-End Agent Learning
August 12, 2026
Latent On-Policy Self-Distillation (LOPD) enables agents to learn from experience by making the teacher's privileged context learnable end-to-end. This removes the need for designer-specified artifacts like hand-crafted feedback or pre-defined skills.
HOW THIS AFFECTS YOU
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builderYou can implement more scalable training loops for autonomous agents using learned supervision.
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researcherThis method enables scaling self-evolving agents without manual prompt or artifact engineering.