Collections
Discover the best community collections!
Collections including paper arxiv:2502.02737
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SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Paper • 2502.02737 • Published • 261 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 263 -
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Paper • 2501.12948 • Published • 462 -
MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 304
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Cosmos World Foundation Model Platform for Physical AI
Paper • 2501.03575 • Published • 84 -
Phi-4 Technical Report
Paper • 2412.08905 • Published • 124 -
MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 304 -
DeepSeek-V3 Technical Report
Paper • 2412.19437 • Published • 88
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STaR: Bootstrapping Reasoning With Reasoning
Paper • 2203.14465 • Published • 9 -
Let's Verify Step by Step
Paper • 2305.20050 • Published • 11 -
Training Large Language Models to Reason in a Continuous Latent Space
Paper • 2412.06769 • Published • 94 -
Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
Paper • 2411.14405 • Published • 62
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Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?
Paper • 2502.00674 • Published • 13 -
Demystifying Long Chain-of-Thought Reasoning in LLMs
Paper • 2502.03373 • Published • 57 -
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Paper • 2502.02737 • Published • 261 -
DeepRAG: Thinking to Retrieval Step by Step for Large Language Models
Paper • 2502.01142 • Published • 25
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MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 304 -
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 290 -
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
Paper • 2412.13663 • Published • 168 -
Apollo: An Exploration of Video Understanding in Large Multimodal Models
Paper • 2412.10360 • Published • 148
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MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models
Paper • 2501.02955 • Published • 44 -
2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining
Paper • 2501.00958 • Published • 110 -
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
Paper • 2501.12380 • Published • 82 -
VideoWorld: Exploring Knowledge Learning from Unlabeled Videos
Paper • 2501.09781 • Published • 27
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Phi-4 Technical Report
Paper • 2412.08905 • Published • 124 -
Evaluating and Aligning CodeLLMs on Human Preference
Paper • 2412.05210 • Published • 48 -
Evaluating Language Models as Synthetic Data Generators
Paper • 2412.03679 • Published • 47 -
Yi-Lightning Technical Report
Paper • 2412.01253 • Published • 29
-
Rethinking Mixture-of-Agents: Is Mixing Different Large Language Models Beneficial?
Paper • 2502.00674 • Published • 13 -
Demystifying Long Chain-of-Thought Reasoning in LLMs
Paper • 2502.03373 • Published • 57 -
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Paper • 2502.02737 • Published • 261 -
DeepRAG: Thinking to Retrieval Step by Step for Large Language Models
Paper • 2502.01142 • Published • 25
-
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Paper • 2502.02737 • Published • 261 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 263 -
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Paper • 2501.12948 • Published • 462 -
MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 304
-
MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 304 -
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 290 -
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
Paper • 2412.13663 • Published • 168 -
Apollo: An Exploration of Video Understanding in Large Multimodal Models
Paper • 2412.10360 • Published • 148
-
Cosmos World Foundation Model Platform for Physical AI
Paper • 2501.03575 • Published • 84 -
Phi-4 Technical Report
Paper • 2412.08905 • Published • 124 -
MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 304 -
DeepSeek-V3 Technical Report
Paper • 2412.19437 • Published • 88
-
MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models
Paper • 2501.02955 • Published • 44 -
2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining
Paper • 2501.00958 • Published • 110 -
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
Paper • 2501.12380 • Published • 82 -
VideoWorld: Exploring Knowledge Learning from Unlabeled Videos
Paper • 2501.09781 • Published • 27
-
STaR: Bootstrapping Reasoning With Reasoning
Paper • 2203.14465 • Published • 9 -
Let's Verify Step by Step
Paper • 2305.20050 • Published • 11 -
Training Large Language Models to Reason in a Continuous Latent Space
Paper • 2412.06769 • Published • 94 -
Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions
Paper • 2411.14405 • Published • 62
-
Phi-4 Technical Report
Paper • 2412.08905 • Published • 124 -
Evaluating and Aligning CodeLLMs on Human Preference
Paper • 2412.05210 • Published • 48 -
Evaluating Language Models as Synthetic Data Generators
Paper • 2412.03679 • Published • 47 -
Yi-Lightning Technical Report
Paper • 2412.01253 • Published • 29