Tensor Product Attention Is All You Need (arxiv.org)

Screenshot of arxiv.org: Tensor Product Attention Is All You Need

First screen, captured 1 year ago.

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Screenshot of arxiv.org: Tensor Manipulation Unit (TMU): Reconfigurable, Near-Memory, High-Throughput AI

Tensor Manipulation Unit (TMU): Reconfigurable, Near-Memory, High-Throughput AI (arxiv.org)

51 points by transpute 1 year ago | 7 comments

Screenshot of arxiv.org: TransMLA: Multi-head latent attention is all you need

TransMLA: Multi-head latent attention is all you need (arxiv.org)

113 points by ocean_moist 1 year ago | 34 comments

Screenshot of aclanthology.org: Native Sparse Attention

Native Sparse Attention (aclanthology.org)

127 points by CalmStorm 1 year ago | 25 comments

Screenshot of arxiv.org: Continuous Autoregressive Language Models

Continuous Autoregressive Language Models (arxiv.org)

98 points by Anon84 11 months ago | 7 comments

Screenshot of arxiv.org: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL (arxiv.org)

1116 points by gradus_ad 1 year ago | 932 comments

arXiv abstract page for the DeepSeek-AI paper introducing DeepSeek-R1, which uses reinforcement learning to improve reasoning in large language models.

deepseek-r1 deepseek reinforcement-learning llm-reasoning

Screenshot of arxiv.org: Multi-Token Attention

Multi-Token Attention (arxiv.org)

131 points by fzliu 1 year ago | 35 comments

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Tensor Manipulation Unit (TMU): Reconfigurable, Near-Memory, High-Throughput AI(arxiv.org)

TransMLA: Multi-head latent attention is all you need(arxiv.org)

Native Sparse Attention(aclanthology.org)

Continuous Autoregressive Language Models(arxiv.org)

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL(arxiv.org)

Multi-Token Attention(arxiv.org)