… To stabilize training, we introduce a phased strategy that first uses distribution matching to bootstrap a stable one-step generator, providing a warm-up phase that brings the student distribution closer to the teacher before adversarial distillation begins. …
… Distilling such generators from high-fidelity bidirectional teachers yields competitive few-step models, yet a persistent gap between the history distributions encountered during training and those arising at inference constrains generation quality over long horizons. …
… In heterogeneous training systems , the total importance ratio should ideally be decomposed into two semantically distinct factors: a training--inference discrepancy term that aligns inference-side and training-side distributions at the same behavior-policy version, and a policy-staleness term that… …
… Existing counting models are often tailored to scenarios such as crowds, vehicles, cells, crops, or remote-sensing objects, and thus struggle to generalize across categories, visual domains, object scales, and density distributions. …
… We hope this data-centric study encourages broader adoption of synthetic data as a practical foundation for layered design editing systems. …
… PASA operates on semantic clusters in a latent embedding space and constructs a distributional dependency between token and auxiliary sequences via shared randomness synchronized by a secret key and semantic history . …
… Aesthetic quality and popularity capture complementary aspects of music that together prove valuable: in an out-of-distribution evaluation on the Music Arena dataset, comprising pairwise human preference battles across eleven generative music systems unseen during training, including aesthetic feat… …
… The following papers were recommended by the Semantic Scholar API Edge of Stability Selectively Shapes Learning Across the Data Distribution 2026 Practical Scaling Laws: Converting Compute into Performance in a Data-Constrained World 2026 Feature Lottery? …
… The following papers were recommended by the Semantic Scholar API $R \text{dm}$: Re-conceptualizing Distribution Matching as a Reward for Diffusion Distillation 2026 S-GRPO: Unified Post-Training for Large Vision-Language Models 2026 OP-GRPO: Efficient Off-Policy GRPO for Flow-Matching Models 2026 … …
… Yeah, forcing old log prob = log prob.detach does not solve the on policy issue since the prob is using current policy but sampling distribution can be different due to expert selection. …