arXiv: cs.AI preprintsInternational5 November 2026
Reasoning-Trace Overflow in Code Generation: An Empirical Study of Explicit Reasoning Models
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arXiv:2611.06095v1 Announce Type: new Abstract: Explicit reasoning can help language models generate code, but it also introduces a practical risk: the model may spend so much of its output budget reasoning that it never produces usable code. We study this behaviour in relatively small open-weight reasoning models, framing it as reasoning-trace overflow. Across seven models and six code benchmark settings, we measure how much of a fixed 32k-token output budget is spent on reasoning, whether that reasoning improves pass@1, and whether lightweight inference-time controls can mitigate overflow. O
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