Paper Detail
Gijs Kassenaar, Zhao Yang, Vincent François-Lavet
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, together with hard per-mode token caps that keep the modes distinct. On a 1.5B distilled model trained on MATH, the three modes emerge without collapsing to a single choice, and the brief modes end up more accurate than \textsc{Long}, which shows that the router sorts problems by difficulty rather than at random. Averaged over three seeds, the resulting policy stays close to the base model's accuracy on the held-out MATH500 ($0.782$ vs.\ $0.796$) while cutting the mean response length from $4{,}796$ to $2{,}811$ tokens (a $41\%$ reduction). Interestingly, it also transfers to other benchmarks without retraining, with the largest savings where problems are easier, with for instance 76\% token reduction on GSM8K and at higher accuracy than the baselines at similar response length. In short, we build a reasoning model that adaptively chooses how much to reason for each problem.
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@article{kassenaar2026learning,
title = {Learning When to Think: Adaptive Reasoning for Test-Time Compute Allocation},
author = {Gijs Kassenaar and Zhao Yang and Vincent François-Lavet},
year = {2026},
abstract = {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: \textbackslash{}textsc\{NoThink\} (answer as quickly as possible), \textbackslash{}textsc\{Short\} (brief reasoning), or \textbackslash{}textsc\{Long\} (extended r},
url = {https://arxiv.org/abs/2608.20256},
keywords = {cs.AI},
eprint = {2608.20256},
archiveprefix = {arXiv},
}
{}