Behind Video Generation: Observations and Insights on Multimodal Understanding from Kling

Behind Video Generation. Observations and Insights on Multimodal Understanding from Kling. By Kling Team and fifty-two others, from the Kling Team. Published on the third of November.

Video generation has moved past isolated clips. It now produces coherent short-form audiovisual sequences, and as that happened the bottleneck shifted. Rendering is no longer the hard part. Semantic control is. This report gathers the observations and insights on multimodal understanding that accumulated across successive generations of Kling. Its central argument is a framing. Generation quality, the authors say, is the projection of multimodal understanding through an entire chain: data, conditioning, alignment, and evaluation. Whatever the generator produces passes through every link of that chain first. Read that way, a video model is only as controllable as the understanding wrapped around it: what the training data says about each clip, how a request is turned into conditioning, what the alignment step rewards, and what the evaluation is able to notice.

The report walks that chain module by module, and one model sits underneath all of it. Kling-MLLM is a shared multimodal foundation model that unifies image, video, audio, synchronized audio and video, and interleaved inputs, and it is specialized for each downstream module. Captioning comes first. Because a DiT is trained on far fewer samples than a language model, captions are treated as a semantic contract whose job is to maximize description density. That contract is then extended to reference-grounded captioning, which makes reference bindings explicit.

Prompt enhancement translates what a user intends into that caption distribution. The basic version is derived from captions of compelling videos, then refined with reinforcement learning at the text level and with the generator in the loop. The agentic version goes further: it refines the story and retrieves compelling examples. For the text encoder, the report finds that reliability comes from perceptual training rather than from stronger language ability, and that candidates can be screened from text alone before any end-to-end training. Reward modeling pairs two models. A general reward model gives explainable, localized critiques. A decision reward model gives fast scoring across multiple dimensions. Last comes evaluation, where the review covers task-specific automatic protocols, focusing on ones that expose scoring shortcuts and help trace a failure back to its upstream source.

Two threads run through these modules. One is the framing already stated: generation quality reflects understanding at every link, in what gets described, how it conditions the generator, how it is rewarded and how it is measured. The other thread is perception. It shows up in the text encoder, where perceptual training matters more than language strength, and again in reward modeling, where perceptual bandwidth is treated as a first-class design parameter rather than an afterthought.

The report offers lessons rather than a single headline result, and that suits its subject. None of these modules is the generator itself. Each one is a place where understanding either reaches the generator or gets lost before it does. The stated hope is modest and concrete: that these lessons give practical guidance to anyone building the understanding stack behind controllable video generation.