Language models can learn to self-allocate reasoning effort by choosing between predefined modes, reducing wasted computation on easy problems while maintaining performance—a practical way to make reasoning models more efficient without separate routing networks.
This paper shows that reasoning language models can learn to adaptively choose how much to think for each problem. Rather than using a fixed token budget, the model picks one of three modes at the start (quick answer, brief reasoning, or extended reasoning) and learns which mode fits each problem's difficulty.