Every Token Needs Its Own Teacher: Multi-teacher Adaptive On-policy Self-distillation

In 30 Sekunden

Was passiert ist

Researchers propose MTA-OPSD, a method where language models select different teacher models token-by-token during training rather than using one fixed teacher.

Warum es zählt

Matters for engineers optimizing large language model training, especially those working on post-training refinement and self-distillation approaches.

Achtung

Results shown only on Qwen3 models for mathematical reasoning tasks. Generalization to other model sizes, architectures, and task domains remains unclear.