Latent On-Policy Self-Distillation Enables Self-Improving Agents
burkov · x · 2026-08-20
Researchers from BUPT, NUS, and SJTU propose "Latent On-Policy Self-Distillation" to help AI agents turn successful attempts into lasting improvements.
Mechanism:
- A student model attempts a task while a copy acts as teacher.
- The teacher receives compressed vectors of past attempts (latent tokens) to guide the student.
- A constraint keeps the teacher more informative, preventing vector degradation.
- Deployment uses only the student model without storing experiences.
Results: The method outperforms baselines on tool-use and coding tests across three relatively small LLMs.
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