NVIDIA LoGRA Paper Cuts RL Training Memory Below LoRA Levels
The headline result is that full-parameter RL updates can fit in less memory than LoRA, removing a common excuse for freezing weights. Engineers blocked by GPU memory when running RL on larger models can adopt LoGRA and scale step sizes via its KL-budget control instead. Compared with rank-256 LoRA, it trades roughly two points of Pass @1 for nearly half the memory at 7B.
