Video understanding represents the most challenging frontier in computer
vision, requiring models to reason about complex spatiotemporal relationships,
long-term dependencies, and multimodal evidence. The recent emergence of
Video-Large Multimodal Models (Video-LMMs), which integrate visual encoders
with powerful decoder-based language models, has demonstrated remarkable
capabilities in video understanding tasks. However, the critical phase that
transforms these models from basic perception systems into sophisticated
reasoning engines, post-training, remains fragmented across the literature.
This survey provides the first comprehensive examination of post-training
methodologies for Video-LMMs, encompassing three fundamental pillars:
supervised fine-tuning (SFT) with chain-of-thought, reinforcement learning (RL)
from verifiable objectives, and test-time scaling (TTS) through enhanced
inference computation. We present a structured taxonomy that clarifies the
roles, interconnections, and video-specific adaptations of these techniques,
addressing unique challenges such as temporal localization, spatiotemporal
grounding, long video efficiency, and multimodal evidence integration. Through
systematic analysis of representative methods, we synthesize key design
principles, insights, and evaluation protocols while identifying critical open
challenges in reward design, scalability, and cost-performance optimization. We
further curate essential benchmarks, datasets, and metrics to facilitate
rigorous assessment of post-training effectiveness. This survey aims to provide
researchers and practitioners with a unified framework for advancing Video-LMM
capabilities. Additional resources and updates are maintained at:
https://github.com/yunlong10/Awesome-Video-LMM-Post-Training