Abstract: Pre-training of world models is key to improving the sample efficiency of reinforcement learning.However,existing methods struggle to capture the causal mechanisms of state transitions due to the lack of explicit action labels in video data.This paper presents MAPO(Multimodal-large-model-generated Action-based pre-training from videOs for world models),a novel pre-training framework.It leverages the semantic understanding of visual-language models and meets the needs of kinematic mode-ling,overcoming the limitations of traditional pre-training methods in the absence of action semantics.MAPO uses the multimodal large model(QWEN2_5-VL-7B) to analyze video frame sequences and generate fine-grained semantic action descriptions during pre-training.This establishes action-state associations with causal explanations.It also designs a context quantization encoding mechanism to separate static scene features from dynamic control factors,improving cross-modal representation.During fine-tu-ning,MAPO uses a dual-network collaborative architecture to align the pre-trained kinematic features with real-environment actions.Experiments show MAPO steadily improves average returns over baselines in 8 tasks on DeepMind Control Suite and Meta-World,especially in long-horizon tasks.This study offers a new cross-modal world model training approach,highlighting the importance of semantic action generation in causal reasoning.

Key words: World models, Reinforcement learning, Video pre-training, Multi-modal large models, Semantic action generation