Background noise in the listening stage often affects the speech intelligibility and quality of communication devices, such as mobile phones. Traditional approaches like Near-end Listening Enhancement (NELE) aimed at processing speech signals to enhance intelligibility. Recent studies have been conducted to enhance intelligibility by converting normal speech to Lombard speech with varying level. However, overzealous focus on intelligibility improvement in previous research led to over-processing, causing speech distortion and quality degradation. Motivated by soft-label guidance, we propose a noise-adaptive fine-grained speech intelligibility enhancement framework—NELE-Diff. It fine-tunes Lombard intensity based on noise, incorporating multi-metric reinforcement learning into the diffusion model reverse process. Subjective and objective experiments reveal the superiority of NELE-Diff over baselines, presenting a more adaptive fine-grained speech intelligibility enhancement framework for different noise levels.