STORM: Internalized Modeling for Spatial-Temporal Reasoning in Video-Language Models
Trains a video-language model to answer through a bounded sequence of continuous hidden states. First aligns these states with features from generated thought videos, then trains with answer-only supervision; inference uses the latent sequence without generating intermediate videos.
First public: · Added to this list: November 2, 2026
Explore this approach
Internalized reasoning — Learn from worked solutions so that some or all of the reasoning can later happen without writing those steps. Training may use a separate teacher model or progressively remove written steps from the model being trained.
Continuous-state reasoning — Use vectors—lists of numbers—to carry intermediate reasoning from one step to the next, instead of selecting a text token at every step. Some methods use the model's hidden states; others combine several candidate token representations.