VideoGen-Agent uses multitask reinforcement learning to teach a video-generation agent to call retrieval, simulation, and verification tools, lifting its base generator from 56.5 to 75.6 on VABench and to 86.1 with upgraded generation tools
The work introduces VideoGen-Agent, a multimodal agent trained with supervised fine-tuning followed by multitask agentic reinforcement learning to coordinate augmentation, generation, and verification tools across six video-generation tasks (Procedural Knowledge, Single-Entity Identity, Multi-Entity Identity, Physics Simulation, Compositional Scene, and Multi-Shot), and builds VABench, a held-out benchmark of 600 prompts; on VABench it improves the base T2V generator Seedance 1.0 from 56.5 to 75.6, and further to 86.1 when compatible upgraded generation tools unseen during training are used, with human raters preferring that configuration in 100 pairwise comparisons.