MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
Paper • 2506.13585 • Published • 276
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sriting
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MiniMax-AI
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liddalidd
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liddalidd
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IanZhong
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LASP-2: Rethinking Sequence Parallelism for Linear Attention and Its Hybrid
Paper • 2502.07563 • Published • 23
You Only Scan Once: Efficient Multi-dimension Sequential Modeling with LightNet
Paper • 2405.21022 • Published
Rethinking RL Scaling for Vision Language Models: A Transparent, From-Scratch Framework and Comprehensive Evaluation Scheme
Paper • 2504.02587 • Published • 32
IanZhong
authored 9
papers over 1 year ago
Scaling TransNormer to 175 Billion Parameters
Paper • 2307.14995 • Published • 23
Exploring Transformer Extrapolation
Paper • 2307.10156 • Published • 1
CO2: Efficient Distributed Training with Full Communication-Computation Overlap
Paper • 2401.16265 • Published • 1
Linear Attention Sequence Parallelism
Paper • 2404.02882 • Published • 3
HGRN2: Gated Linear RNNs with State Expansion
Paper • 2404.07904 • Published • 20
Various Lengths, Constant Speed: Efficient Language Modeling with Lightning Attention
Paper • 2405.17381 • Published
Scaling Laws for Linear Complexity Language Models
Paper • 2406.16690 • Published • 23
Audio-Visual Segmentation with Semantics
Paper • 2301.13190 • Published
MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 303
liddalidd
authored 2
papers over 1 year ago
MiniMax-AI
authored a
paper over 1 year ago