RegMix-D fits regression models to proxy loss trajectories to produce dynamic data mixture schedules that outperform static RegMix and DoReMi on 25B-token Pile pretraining with a 1B model.
We use the Pile-CC validation loss as the target predicted loss
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RegMix-D: Dynamic Data Mixing via Proxy Training Trajectories
RegMix-D fits regression models to proxy loss trajectories to produce dynamic data mixture schedules that outperform static RegMix and DoReMi on 25B-token Pile pretraining with a 1B model.