CapRL: Reinforcement Learning Boosts Image Captioning Quality
Researchers have developed CapRL, a novel reinforcement learning framework for image captioning that significantly improves vision-language alignment. The team, led by Long Xing and Xiaoyi Dong, has demonstrated substantial gains across twelve benchmarks, matching the performance of state-of-the-art models like Qwen2.5-VL-72B.
CapRL's innovative approach addresses the fundamental task of bridging the visual and linguistic domains in Large Vision-Language Models (LVLMs). Unlike current models that rely heavily on supervised learning and human-annotated data, CapRL improves caption quality by encouraging models to generate detailed and precise image descriptions. This is achieved through a reinforcement learning with verifiable rewards (RLVR) paradigm, which overcomes the limitations of supervised fine-tuning (SFT) that often leads to memorisation and lack of creativity.
CapRL defines caption quality by the utility of the generated caption. A high-quality caption should enable an independent system to accurately answer questions about the corresponding image. The model's performance is evaluated using a question-answering approach, where a separate language model answers questions based solely on the generated caption. Pretraining on the CapRL-5M caption dataset, annotated by CapRL-3B, has resulted in these substantial gains across multiple benchmarks.
CapRL's success in improving vision-language alignment and generating high-quality captions opens up new possibilities for Large Vision-Language Models. By redefining caption quality and employing reinforcement learning techniques, CapRL has shown promising results comparable to state-of-the-art models, marking a significant advancement in the field of image captioning.
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