When More Thinking Hurts: Overthinking in LLM Test-Time Compute Scaling
Scaling test-time compute through extended chains of thought has become a dominant paradigm for improving large language model reasoning. However, existing research implicitly assumes that longer thinking always yields better results. This assumption remains largely unexamined. We systematically investigate how the marginal utility of additional reasoning tokens changes as compute budgets increase. We find that marginal returns diminish substantially at higher budgets and that models exhibit ``overthinking'', where extended reasoning is associated with abandoning previously correct answers. Furthermore, we show that optimal thinking length varies across problem difficulty, suggesting that...
| Authors | Shu Zhou, Rui Ling, Junan Chen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 April 2026 |
| Last updated at source | 12 April 2026 |
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