  In this work, we present an unsupervised method for enhancing an image
captioning model (in our case, BLIP2) using reinforcement learning and
vision-language models like CLIP and BLIP2-ITM as reward models. The RL-tuned
model is able to generate longer and more comprehensive descriptions. Our model
reaches impressive 0.90 R@1 CLIP Recall score on MS-COCO Carpathy Test Split.
  Weights are available at
https://huggingface.co/sashakunitsyn/vlrm-blip2-opt-2.7b.
