Decomposing Reasoning Efficiency in Large Language Models

• URL: http://arxiv.org/abs/2602.09805v1
• Date: Tue, 10 Feb 2026 14:09:18 GMT
• Title: Decomposing Reasoning Efficiency in Large Language Models
• Authors: Daniel Kaiser, Arnoldo Frigessi, Ali Ramezani-Kebrya, Benjamin Ricaud,
• Abstract summary: We decompose token efficiency into interpretable factors: completion under a fixed token budget, conditional correctness given completion, and verbosity.<n>When reasoning traces are available, we add deterministic trace-quality measures to separate looping from verbose-but-engaged reasoning.<n>Our decomposition reveals distinct bottleneck profiles that suggest different efficiency interventions.
• Score: 2.4149105714758545
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models trained for reasoning trade off inference tokens against accuracy, yet standard evaluations report only final accuracy, obscuring where tokens are spent or wasted. We introduce a trace-optional framework that decomposes token efficiency into interpretable factors: completion under a fixed token budget (avoiding truncation), conditional correctness given completion, and verbosity (token usage). When benchmark metadata provides per-instance workload proxies, we further factor verbosity into two components: mean verbalization overhead (tokens per work unit) and a coupling coefficient capturing how overhead scales with task workload. When reasoning traces are available, we add deterministic trace-quality measures (grounding, repetition, prompt copying) to separate degenerate looping from verbose-but-engaged reasoning, avoiding human labeling and LLM judges. Evaluating 25 models on CogniLoad, we find that accuracy and token-efficiency rankings diverge (Spearman $ρ=0.63$), efficiency gaps are often driven by conditional correctness, and verbalization overhead varies by about 9 times (only weakly related to model scale). Our decomposition reveals distinct bottleneck profiles that suggest different efficiency interventions.

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