Can generative models also be trained end-to-end? The core turns out to be a for loop. In 2012, AlexNet ended an era with a landslide victory. Before that, image recognition relied on manually designed hierarchical feature extraction pipelines; AlexNet proved a fact that was repeatedly verified later: handing over the entire task to the model for self-learning end-to-end almost always outperforms the carefully designed phased pipelines by humans. From image classification to object detection and then to image segmentation, behind every leap of deep learning is the same logic: let it learn the whole task in one go. There has always been only one exception: generative models. The most powerful and scalable generative models today (whether autoregressive or diffusion models) are not end-to-end. During training, they only learn to predict "one small step", but during inference, they need to unfold this step repeatedly hundreds or thousands of times like a recurrent network. Training and inference do not use the same sampling method. This discrepancy brings up an old problem: the error of each step is fed into the next step, the input gradually drifts away from the distribution seen during training, and errors accumulate layer by layer. Academically, this is called "exposure bias". In other words, "end-to-end performs better" — this core experience of deep learning — has never been truly applied to generative models for more than a decade. Recently, a paper from UIUC and Harvard University attempts to fill this last piece of the puzzle. The authors named this new paradigm Explorative Modeling, abbreviated as XM. Its idea is so simple that it seems almost naive, but it points to a bold conclusion: In addition to parameters and data, generative models actually have a third amplifiable dimension. Project website: https://explorative-modeling.github.io Paper link: https://arxiv.org/abs/2607.27372 Code repository: https://github.com/alexiglad/XM Root of