MSA is an end-to-end trainable memory model using sparse attention and document-wise RoPE that scales to 100M tokens with linear complexity and less than 9% degradation.
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3 Pith papers cite this work. Polarity classification is still indexing.
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In linear regression, LoRA can achieve lower excess risk than full fine-tuning when the pretraining-downstream difference is low-rank, and small LoRA ranks can improve generalization by acting as regularization.
GoodServe proposes a predict-and-rectify routing system for agentic LLM inferences on heterogeneous GPUs that improves goodput by up to 27.4%.
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LoRA vs. Full Fine-Tuning: A Theoretical Perspective
In linear regression, LoRA can achieve lower excess risk than full fine-tuning when the pretraining-downstream difference is low-rank, and small LoRA ranks can improve generalization by acting as regularization.