Parameter-Efficient Fine-Tuning (PEFT) adapts large language models (LLMs) by training only a small fraction of parameters. %Adapter-based approaches reduce compute per step but introduce practical overhead from the additional adapter path (e.g., extra kernel launches and activation storage). Adapter-based approaches reduce the number of trainable parameters, but introduce practical overhead from the additional adapter path (e.g., extra kernel launches and activation storage). Adapter-free approaches avoid this structural overhead by directly updating pretrained weights; however, random index selection can fragment the trainable subspace, while calibrated weight selection incurs extra cost, limiting accuracy or efficiency. We propose Recursively Aligned Pathway Adaptation (RAPA), an adapter-free PEFT method that forms index-consistent pathways through depth. RAPA follows two principles: (i) selecting balanced submatrices that maximize the number of weights alignable across layers, and (ii) recursively aligning these indices across layers and residual connections. In experiments, RAPA matches or surpasses strong PEFT baselines across most benchmarks while preserving adapter-free efficiency with minimal memory and compute overhead.