Projection-Level LoRA Placement: An Equal-Parameter Audit Across Models and Tasks

Choosing which transformer projections receive LoRA adapters changes both parameter budget and training behavior. We audit placement under matched trainable-parameter budgets using Llama-2-7b-chat-hf and Llama-3.1-8B-Instruct, Alpaca and GSM8K, and three seeds per setting. The resulting 36-run matrix compares up-only, down-only and a training-data-based PLoP allocation. Up-only has the lowest mean response NLL in one of four model–task combinations; down-only wins the other three. These comparisons measure teacher-forced response likelihood. We also audit generated numeric answers at all 18 GSM8K adapters and two base checkpoints on separate internal and official-test subsets; prompt compatibility and termination limit interpretation of raw base scores. An earlier 25-configuration sweep favored up-only on reused WikiText-2 validation of candidate choices, but did not match parameter budgets. On the official-test subset, up-minus-down accuracy is +3.57 percentage points for Llama-2 (pointwise 95% interval [1.30,5.87]) and -0.50 for Llama-3 ([-3.00,1.97]). Historical and matched protocols differ in objective and holdout construction, so their ordering changes are not a placement-only intervention. Our results support task- and metric-dependent evaluation of placement, not a universally best projection or a new module-selection algorithm.