Accelerating Thinking via Distillation and Multi-Model Training
_arohan_ · x · 2026-07-12
The author shares ongoing experiments to train N 个模型 to achieve nearly N 倍加速, raising several key questions:
- Why does distillation lead to a loss in performance gains?
- How can we transfer more "identifiable" information across networks?
- How can each network match others' outputs while retaining its own judgment?
This explores research thoughts surrounding multi-model training and distillation efficiency.
Related event: Knowledge Distillation Accelerates Distributed Training(2 posts)→
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