Papers

Filtered to one-step generators · clear filter

Browse by term

continual learning 64reinforcement learning 34large language models 17benchmarking 12vision-language models 10generative models 8language models 8video generation 7multimodal models 6natural language processing 6robotics 6world models 6benchmarks 5diffusion models 5on-policy distillation 5policy optimization 5scalability 5self-distillation 5vision-language-action models 5autoregressive models 4computer vision 4diffusion transformers 4LLMs 4multimodal large language models 4verifiable rewards 4attention mechanisms 3embodied intelligence 3image editing 3long-term memory 3multimodal learning 3

Matching papers

Three-Body Scattering for Generative Modeling

12 upvotes · 20 JUL 2026 · Peng Sun, Zhenglin Cheng, Deyuan Liu et al.

This paper introduces Three-Body Scattering Modeling (TBSM), a new approach to generative modeling that uses a distributional energy to induce motion and provide direct regression supervision for one-step generators. Practitioners might care because TBSM can achieve high-quality generation with reduced field noise and fewer sample-level losses.

generative modelsone-step generatorsdistributional energyWasserstein distancediffusion-based methods