Tree Training serializes tree trajectories via DFS and uses redundancy-free partitioning to compute weighted per-token losses exactly once per token, achieving up to 6.2x training speedup on dense and MoE models.
Flashmask: Efficient and rich mask extension of flashattention.arXiv preprint arXiv:2410.01359, 2025
4 Pith papers cite this work. Polarity classification is still indexing.
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STOF accelerates sparse Transformer inference on GPUs by pairing a unified multi-head attention kernel with flexible, auto-tuned operator fusion.
FlexAttention supplies a compiler-driven interface that expresses common attention variants in a few lines of PyTorch and emits optimized kernels whose speed matches hand-written implementations.
A roadmap that defines architectural nativity for multimodal models and categorizes them into Multi-to-Text, Multi-to-Target, and Multi-to-Multi types while outlining an industrial pipeline toward unified transformer-based native multimodal modeling.
citing papers explorer
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Tree Training: Accelerating Agentic LLMs Training via Shared Prefix Reuse
Tree Training serializes tree trajectories via DFS and uses redundancy-free partitioning to compute weighted per-token losses exactly once per token, achieving up to 6.2x training speedup on dense and MoE models.
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Accelerating Sparse Transformer Inference on GPU
STOF accelerates sparse Transformer inference on GPUs by pairing a unified multi-head attention kernel with flexible, auto-tuned operator fusion.
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Flex Attention: A Programming Model for Generating Optimized Attention Kernels
FlexAttention supplies a compiler-driven interface that expresses common attention variants in a few lines of PyTorch and emits optimized kernels whose speed matches hand-written implementations.
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Toward Native Multimodal Modeling: A Roadmap
A roadmap that defines architectural nativity for multimodal models and categorizes them into Multi-to-Text, Multi-to-Target, and Multi-to-Multi types while outlining an industrial pipeline toward unified transformer-based native multimodal modeling.