ByteDance has announced a commitment to developing its next-generation AI model without relying on AI distillation techniques.
The company intends to pursue an independent development path for its future models.
ByteDance has announced a commitment to developing its next-generation AI model without relying on AI distillation techniques.
The company intends to pursue an independent development path for its future models.
MiniMax released the open-weight H3 omni-modal video generation model on Hugging Face, featuring native stereo audio that can drive video in a single forward pass. The model supports 2K resolution at 24fps for clips lasting 5 to 15 seconds and accepts up to nine reference images, three videos, and three audio clips per generation.
Researchers propose Unsupervised On-Policy Self-Distillation (U-OPSD), a method that achieves post-training improvement for large language models using only the model's own generations without external supervision. The approach samples multiple rollouts to construct a pseudo-solution via majority vote, then distills a teacher distribution into prefixes of the model's longest incorrect completion.
Douyin has released the technical report for its Multimodal Embedding (DME) model, which combines large-scale contrastive pre-training with latent reasoning to achieve strong discrimination and efficiency.
Apple has introduced a new architecture for its third-generation foundation models (AFM3) that utilizes "Instruction-Following Pruning" to significantly reduce active parameter count. The model is designed to activate approximately 20% of its MLP layers by making routing decisions once per prompt rather than per token.
NVIDIA's NeMo team has released Molt, an open-source agentic reinforcement learning framework designed to reduce the complexity of algorithm iteration for researchers. The codebase is approximately 8.6K lines of RL code, significantly smaller than comparable frameworks like verl (62K lines) and slime (25K lines).
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