Using EMA on LoRA Adapters for Self-Distillation
Learn if EMA can stabilize LoRA training and improve model performance via self-distillation.
30-Second TL;DR
What Changed
Investigating EMA as a self-teacher mechanism for parameter-efficient fine-tuning
Why It Matters
If successful, this approach could improve the stability and performance of LoRA fine-tuning without the computational cost of full model updates.
What To Do Next
Review the referenced paper on on-policy self-distillation and attempt a small-scale experiment applying EMA to your LoRA rank-update matrices.
Key Points
- •Investigating EMA as a self-teacher mechanism for parameter-efficient fine-tuning
- •Comparing LoRA-based self-distillation against full fine-tuning methods
- •Seeking empirical evidence for EMA effectiveness in low-rank adaptation scenarios
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Original source: Reddit r/MachineLearning ↗
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