CROP: Token-Efficient Reasoning in Large Language Models via Regularized Prompt Optimization

arXiv:2604.14214v1 Announce Type: cross Abstract: Large Language Models utilizing reasoning techniques improve task performance but incur significant latency and token costs due to verbose generation. Existing automatic prompt optimization(APO) frameworks target task accuracy exclusively at the expense of generating long reasoning traces. We propose Cost-Regularized Optimization of Prompts (CROP), an APO method that introduces regularization on response length by generating textual feedback in a