Training Quality Determines Efficiency Boundaries in Test-Time Reasoning

Training quality is the primary determinant of reasoning efficiency in large language models, yielding greater returns than proportional increases in inference computation. Evaluating 44 language models (0.5B--685B parameters) across six reasoning benchmarks totalling over 67000 assessments, this study establishes when additional inference tokens are beneficial, redundant or actively harmful. Standard models exhibit near-universal accuracy convergence at approximately 1000 tokens regardless of architecture or parameter count. Causal intervention experiments demonstrate that constraining generation length improves reasoning accuracy by 15.4 percentage points, identifying over-generation rather than capacity exhaustion as the primary efficiency bottleneck. Among 13 reasoning-specialised models, training methodology yields 18.8-fold efficiency improvement at matched parameter scales, exceeding gains from ten-fold parameter increases. Graduate-level evaluation reveals that arithmetic benchmark performance does not predict reasoning capability on complex tasks ($r = -0.03$, $P = 0.91$), exposing fundamental limitations invisible to standard evaluation practice. Mechanistic profiling reveals two orthogonal dimensions of training quality---quality control and decomposition efficiency---that independently predict efficiency profiles, providing a measurable framework linking training decisions to inference-time behaviour.