DHAFT: Dynamic Hierarchy-Aware Fine-Tuning for Efficient LLM Reasoning

Large language models (LLMs) have achieved remarkable performance on complex reasoning tasks, but efficiently adapting them to diverse reasoning scenarios remains challenging. Soft prompt tuning provides a parameter‑efficient adaptation strategy with a small number of trainable prompt vectors. However, existing approaches overlook the evolving nature of internal representations across model depth, making prompt effectiveness sensitive to different representation stages. To address this issue, we propose DHAFT, a hierarchy‑aware soft prompt tuning framework that dynamically identifies hierarchical representation stages from internal representations and performs stage‑specific prompt injection at representative layers. By aligning prompt placement with representation stages, DHAFT enables more effective prompt utilization and reduces potential cross‑stage interference. Furthermore, we introduce PSMI, a prompt‑stage misalignment index that quantifies the mismatch between prompt influence and representation stages. Extensive experiments across diverse LLM backbones and reasoning benchmarks demonstrate that DHAFT achieves competitive or superior performance compared with representative PEFT methods while maintaining minimal parameter overhead.