# ReverseAdaptive

This work measures per-round upload and download bytes, rather than parameter-count ratios, for five federated LoRA protocols and places them on a single communication-quality frontier scored by held-out instruction-following loss; ReverseAdaptive, which learns both LoRA factors and then freezes one once the relative improvement in training loss falls below a dimensionless threshold, sits at the knee of that frontier, cutting measured round-trip communication by 40.5% relative to FLoRA at a held-out loss cost of 0.0063 and beating FFA-LoRA, which freezes that factor at initialization, by 0.0182 in held-out loss, more than twenty times the largest per-method seed standard deviation, with the same threshold carrying to LLaMA-3.2-3B without retuning for a 30.0% saving.

This work measures per-round upload and download bytes, rather than parameter-count ratios, for five federated LoRA protocols and places them on a single communication-quality frontier scored by held-out instruction-following loss; ReverseAdaptive, which learns both LoRA factors and then freezes one once the relative improvement in training loss falls below a dimensionless threshold, sits at the knee of that frontier, cutting measured round-trip communication by 40.5% relative to FLoRA at a held-out loss cost of 0.0063 and beating FFA-LoRA, which freezes that factor at initialization, by 0.0182 in held-out loss, more than twenty times the largest per-method seed standard deviation, with the same threshold carrying to LLaMA-3.2-3B without retuning for a 30.0% saving.