Efficient reasoning is critical for large reasoning models (LRMs) deployed in agent systems with strict context constraints. Existing methods predominantly rely on global length penalties that overlook step-level structure, inadvertently compressing critical derivations while retaining low-value detours. Hence, we argue for a principled, step-aware alternative that preserves critical steps and compresses low-value detours. However, it raises three fundamental challenges: the difficulty of obtaining step-level supervision, the need for reward signals that induce differentiated length pressure, and the requirement to balance training stability with exploration on complex problems. We propose SmartThinker, a step-level token allocation framework that learns to selectively preserve or compress individual reasoning steps. At its core is Step-Level Length Control Policy Optimization (SCPO), which provides label-free feedback by online estimation of each step’s contribution from the change in correct-answer likelihood after removing it. We further introduce reasoning density, defined as contribution per token, to relax length pressure on high-value steps while intensifying it on low-density ones. Finally, SCPO stabilizes training through length-bias-corrected advantages and difficulty-adaptive clipping, achieving superior accuracy-efficiency trade-offs across nine in-domain and out-of-domain benchmarks.