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LAION releases BVD — 1.3B crawled video URLs, 80M downloaded videos, 10M hours of content, 55M scene-detected and synthetically captioned clips. It's the open-research video equivalent of LAION-5B, and it arrives at the moment when video-trained multimodal models are becoming the competitive baseline.
Read more →A paper from EPFL's dlab and the MATS alignment program proposes threading value-laden reflections through 10% of pretraining documents, showing a 63% reduction in adversarial attack success — but only when post-training uses precisely matching persona-binding templates. The brittleness is the most revealing finding.
Read more →A new pretraining technique called Explorative Modeling adds a best-of-K selection step to the training loop — generate K candidates, keep the one closest to the target, backprop through only that one. The efficiency gains on image and video models are large and grow with scale, suggesting a genuine third axis alongside parameters and data. For autoregressive LLMs the gains are modest for now, but the underlying idea is worth watching.
Read more →A paper submitted yesterday finds that reducing MLP width monotonically from early to late transformer layers — using a cosine schedule — consistently improves performance across three scales and four architectures at zero additional cost. Later layers refine the residual stream rather than transform it, so the standard uniform allocation gives too much capacity to the wrong end of the network.
Read more →Google DeepMind's Vision Banana paper shows that training a model to generate images — and only that — produces transferable visual representations strong enough to beat specialized discriminative models on segmentation and metric depth estimation when lightly instruction-tuned. The finding is the visual analog of how LLM pretraining generalizes across language tasks.
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