Train Wide, Commit Late: Delayed Capacity Commitment for Parameter-Efficient Fine-Tuning

Parameter-efficient fine-tuning (PEFT) typically makes the deployment budget binding before optimization ends. We challenge this coupling and introduce Delayed Capacity Commitment (DCC), a design principle that separates training-time capacity from final deployment commitment. DCC retains a small surplus of candidate capacity after adaptive compression and resolves it only at the end of training, without changing the final deployment budget. We instantiate DCC with Adaptive Competition Policy (ACP), which uses global rank-one competition and training-state-driven compression to govern capacity evolution. We call the paired effect of delayed versus immediate commitment under matched initial and deployment capacities the capacity dividend. On GSM8K, this dividend is consistently positive across five Qwen3-0.6B seeds, reaching +1.60 and +2.50 percentage points at the two tested buffer ratios; Llama-3.1-8B shows +1.95 points at the smaller buffer. Broader evaluations reveal substantial task-, model-, and budget-dependence. These results identify when capacity becomes binding as a distinct design dimension in PEFT.