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Jointly training path and flow networks with a novel alignment objective unlocks improved generative model performance by preventing path overfitting and low-entropy bottlenecks.
Accurate latent reconstruction is a red herring for diffusion-based reasoning; asynchronous denoising over multi-layer teacher representations allows a compact 638M model to solve complex math and code without autoregressive rollouts.
