When Gradient Importance Lies: Adaptive LoRA Rank Allocation Fails Under GRPO

Excerpt

arXiv:2605.07366v2 Announce Type: replace Abstract: Adaptive rank allocation for LoRA - allocating more parameters to important layers and fewer to unimportant ones - consistently improves efficiency under supervised fine-tuning (SFT). We test whether this success transfers to reinforcement learning, specifically Group Relative Policy Optimization (GRPO). Using gradient-magnitude profiling on Qwen 2.5 1.5B with GSM8K, we find that, in our setting, it does not: proportional rank allocation degrad