CODA: Difficulty-Aware Compute Allocation for Adaptive Reasoning
The emergence of large reasoning models demonstrates that scaling inference-time compute significantly enhances performance on complex tasks. However, it often falls into another trap: overthinking simple problems, where repetitive rationales yield minimal accuracy gains at a disproportionately high cost. This motivates adaptive reasoning: dynamically aligning reasoning depth with instance difficulty. In this paper, we study adaptive reasoning from an optimality perspective, formalizing it as a utility maximization problem where tokens are allocated until the marginal accuracy gain falls below the incremental cost. Based on this, we propose CODA (Compute Allocation by Difficulty Awareness...
| Authors | Siye Wu, Jian Xie, Yikai Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 March 2026 |
| Last updated at source | 6 April 2026 |
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