🚀 Introducing Temporal Preference Optimization (TPO) – a video-centric post-training framework that enhances temporal grounding in long-form videos for Video-LMMs! 🎥✨ 🔍 Key Highlights: ✅ Self-improvement via preference learning – Models learn to differentiate well-grounded from inaccurate responses without manual annotations. ✅ Multi-level temporal grounding – Effectively captures both localized segments and comprehensive video sequences. ✅ Efficient and scalable – Utilizes only 10k video QA pairs for post-training and can scale seamlessly to larger datasets. ✅ Proven performance – TPO enhances two state-of-the-art Video-LMMs on LongVideoBench, MLVU, and Video-MME. 🔥 LLaVA-Video-TPO is now the top-performing 7B model on Video-MME, highlighting TPO's potential in advancing temporal reasoning. This work was co-led with the talented undergraduate @rui__li, alongside fantastic collaborators @Zhang_Yu_hui and Zeyu Wang, and advised by @yeung_levy!