…as an expressive conditional normalizing flow with exact likelihood training, enabling high-quality sample generation in few steps while maintaining likelihood framework. AI-generated summary Diffusion-based models decompose sampling into many…
…AI-generated summary Recent generative models can produce images that appear highly realistic, raising challenges in distinguishing real and AI-generated images. Yet existing detectors based on pre-trained feature extractors tend…
…AI-generated summary Training multimodal large language models has long been limited by the scarcity of high-quality paired multimodal data. Recent studies show that the shared representation space of pretrained multimodal…
…This constraint makes training data difficult to curate at scale and limits generalization across diverse edit types. We propose Delta-Adapter, a method that learns transferable editing semantics under single-pair supervision…
…Taekhyun Park , , , Abstract LoopUS is a post-training framework that transforms pretrained LLMs into looped architectures for improved reasoning performance through latent-refinement and adaptive early exiting mechanisms. AI-generated summary Looped…
…Is the 6M cost reported by media just the number taken from v3's training cost? · Ops... Has anyone asked the DeepSeek team to publish their training data and code, or at…
…AI-generated summary We show that Fréchet Distance (FD), long considered impractical as a training objective, can in fact be effectively optimized in the representation space . Our idea is simple: decouple the…
…Ziyun Zeng , , , Abstract A new dataset and benchmark for background replacement in video editing are introduced, addressing limitations in existing datasets through a scalable pipeline with improved guidance mechanisms. AI-generated summary…
…AI-generated summary RLVR and OPD have become standard paradigms for post-training . We provide a unified analysis of these two paradigms in consolidating multiple expert capabilities into a single model, identifying…
…To address this, we propose Semi- DPO , a semi-supervised approach that treats consistent pairs as clean labeled data and conflicting ones as noisy unlabeled data. Our method starts by training on…
