Tag: State Space Model
All the articles with the tag "State Space Model".
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Zebra-Llama: Towards Extremely Efficient Hybrid Models
Published: at 11:34 AM
89.05 🤔Zebra-Llama通过结合状态空间模型和多头潜在注意力层，从预训练Transformer构建高效混合模型，显著降低KV缓存大小并提升推理吞吐量，同时保持或超越基线性能。
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Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanism
Published: at 11:15 PM 87.26 🤔本文通过提出Gather-and-Aggregate (G&A)机制，揭示了Transformer和SSM模型在上下文检索能力上的性能差距主要源于少数关键头部的实现差异，并通过混合模型实验验证了注意力机制在改进SSM检索能力上的潜力。
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How Well Can a Long Sequence Model Model Long Sequences? Comparing Architechtural Inductive Biases on Long-Context Abilities
Published: at 11:24 AM 86.84 🤔本文通过对比实验揭示，尽管长序列模型（如Mamba2）理论上支持无限长上下文，但在实际长上下文任务中与Transformer模型一样面临显著局限，尤其在信息位置和数据格式变化时表现不佳，亟需进一步研究其原因。
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Recall with Reasoning: Chain-of-Thought Distillation for Mamba's Long-Context Memory and Extrapolation
Published: at 06:13 PM 86.84 🤔This paper proposes Recall with Reasoning (RwR), a method that enhances Mamba's long-context memory and extrapolation by distilling chain-of-thought summarization from a teacher model, achieving significant performance improvements on LONGMEMEVAL and HELMET benchmarks while preserving short-context capabilities.
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Scaling Reasoning without Attention
Published: at 11:25 AM 85.88 🤔本文提出 PROMPTCOT-MAMBA，一种基于 Mamba-2 状态空间模型的无注意力语言模型，通过两阶段课程微调和 PROMPTCOT 合成范式，在数学和代码推理任务上超越同规模甚至更大规模的 Transformer 模型，同时实现固定内存和高效推理。