SparseLoCo combines error feedback with Top-k sparsification and 2-bit quantization to send 1-3% of the pseudo-gradient during LLM pre-training while matching or beating DiLoCo's dense updates.
16 Preprint Table 12: Final validation loss for the 178M model while varying the number of workers (R∈ {8,16,32}) and the communication interval (H∈ {15,50,100}).Bestis bold
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Overcoming the Communication-Performance Tradeoff in LLM Pretraining
SparseLoCo combines error feedback with Top-k sparsification and 2-bit quantization to send 1-3% of the pseudo-gradient during LLM pre-training while matching or beating DiLoCo's dense updates.