Title: Rethinking the Value of Prompt Learning for Vision-Language Models. Abstract: Large-scale visual-language pre-training like CLIP has demonstrated great success in open-set visual concept learning that enables zero-shot transfer to downstream tasks through prompting. To automate prompt engineering, prompt learning is proposed to automatically learn the optimal task-relevant prompts. In this paper, we make some surprising observations that contradict common beliefs about prompts. We observe that even random prompts can achieve pretty good performance for zero-shot recognition. We also find that prompt learning gives comparable or worse performance than directly fine-tuning of the linear classifier. Moreover, prompt learning is no more than parameter-efficient learning, and is a trade-off between optimality and generalization. Our results highlight the need for the rethinking of existing prompt learning, more careful baseline evaluations in future research on prompt learning methods in vision-language models. 