Local proxies for memory-efficient neural training
In brief
- Local proxies predict global network outputs via distillation to cut memory needs.
- Method enables independent gradient propagation while closely estimating backpropagation gradients.
- Better accuracy/memory trade-off than existing local algorithms, including LLM fine-tuning.
A new local training method for transformers reduces memory usage by leveraging local proxies. These proxies predict global network outputs via distillation, enabling independent gradient propagation. Results show a better accuracy/memory trade-off than existing local algorithms, including for fine-tuning large language models.
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