InternLM / CapRL

What it does

• Unified dense caption RL for images and videos. CapRL++ applies the same QA-utility reward philosophy to both image and video captioning, avoiding dependence on a single reference caption.
• Verifiable reward design. CapRL++ combines visual utility reward, timestamp-format reward, and length-aware penalty to optimize accuracy, temporal structure, and information efficiency.
• Strong temporal grounding. CapRL-Video-4B generates explicit timestamped video descriptions and improves downstream video understanding when used as caption data.
• Remarkable visual understanding for charts, infographics, and documents: CapRL-3B achieves perception accuracy and visual information coverage comparable to Qwen2.5-VL-72B.
• Well-organized dense output. CapRL models generate structured captions that cover fine-grained objects, attributes, OCR content, relations, and events.