Learning When to Think: Adaptive Reasoning for Test-Time Compute Allocation
Reasoning language models trained with reinforcement learning typically operate under a fixed token budget rather than an explicitly adaptive one, which can lead to over-computation on easy problems and insufficient computation on difficult ones. We study whether a model can learn to allocate its own reasoning effort by choosing, as the first token of its response, one of three modes: \textsc{NoThink} (answer as quickly as possible), \textsc{Short} (brief reasoning), or \textsc{Long} (extended reasoning). The choice is learned inside Group Relative Policy Optimization (GRPO) with no separate router, through a shaped reward that makes each mode worthwhile at a different response length, to...
| Authors | Gijs Kassenaar, Zhao Yang, Vincent François-Lavet |
|---|---|
| Primary category | cs.AI |
| Published | 20 August 2026 |
| Last updated at source | 21 August 2026 |
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