V-JEPA 2 versus Vision-Language Models
V-JEPA 2 and video-language models answer different engineering questions. V-JEPA 2 asks whether latent video prediction can produce reusable visual dynamics and planning-oriented features. A vision-language model asks whether visual tokens can be aligned with language for captioning, question answering, retrieval, or chat. The comparison is objective and interface, not a universal ranking.
Latent prediction versus token generation
V-JEPA-style training minimizes a latent prediction loss:
A language-facing VLM optimizes text or alignment losses, for example
The first produces predictive visual representations; the second produces language-conditioned outputs. World models and JEPA explains why representation-space prediction is treated as a world-modeling route.
Worked comparison
The difference is easiest to see by comparing what each objective scores:
| model family | prediction | target | score being optimized |
|---|---|---|---|
| V-JEPA-style latent prediction | |||
| language-facing VLM | caption logits | answer token/class 1 | softmax probabilities |
The latent objective grades prediction accuracy in representation space; the language objective grades a text-facing answer distribution. A product may need both: visual dynamics for anticipation or planning, and language alignment for captions, retrieval, or question answering.
Practical choice
Use V-JEPA-style representations when the task is anticipation, planning, motion-sensitive transfer, or visual dynamics. Use a VLM when the user interaction is language-first. For many systems, the best design is hybrid: a strong video encoder, temporal retrieval or localization, and a language model for explanation.
References
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Section — Video Understanding