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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 628 -
MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 301 -
Group Sequence Policy Optimization
Paper • 2507.18071 • Published • 315 -
Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth
Paper • 2509.03867 • Published • 210
Collections
Discover the best community collections!
Collections including paper arxiv:2505.09388
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Snowflake/Arctic-Text2SQL-R1-7B
8B • Updated • 11.7k • 57 -
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277 -
Reinforcement Pre-Training
Paper • 2506.08007 • Published • 263 -
Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights
Paper • 2506.16406 • Published • 128
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 628 -
MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 301 -
Group Sequence Policy Optimization
Paper • 2507.18071 • Published • 315 -
Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth
Paper • 2509.03867 • Published • 210
-
Snowflake/Arctic-Text2SQL-R1-7B
8B • Updated • 11.7k • 57 -
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277 -
Reinforcement Pre-Training
Paper • 2506.08007 • Published • 263 -
Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights
Paper • 2506.16406 • Published • 128