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Group Variance Policy Optimization is introduced, a novel post-training method that integrates the analytical solution of KL-constrained reward maximization into its gradient weighting scheme and establishes a new paradigm for reliable and versatile LLM post-training and on-policy distillation.
A memory-free recent-window protocol is fixed and a transparent and reproducible reference for open-source streaming-video research is provided, combining verifiable streaming-video data, thinking-mode OPD, and instruct-mode deployment.
Mage-VL is presented, an efficient codec-native streaming foundation model for real-time multimodal understanding and interaction and establishes AI4AI data pipelines encompassing prompt-code joint optimization for multimodal captioning and AI-driven performance diagnosis to guide training recipes.
This work reformulates the video diffusion sampling as a frame-indexed stochastic process over noise levels, and constructs a continuous training trajectory along which the sampling schedule progressively evolves from independent sampling to inference-consistent sampling.
The results show that careful tokenizer--backbone--system co-design can deliver strong high-resolution generation and editing within an efficient 4B model family.
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