Differential Transformer
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2410.05258
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Published
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169
Great paper
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PaliGemma 2: A Family of Versatile VLMs for Transfer
Paper • 2412.03555 • Published • 126 -
VisionZip: Longer is Better but Not Necessary in Vision Language Models
Paper • 2412.04467 • Published • 106 -
o1-Coder: an o1 Replication for Coding
Paper • 2412.00154 • Published • 43 -
SNOOPI: Supercharged One-step Diffusion Distillation with Proper Guidance
Paper • 2412.02687 • Published • 109 -
TAPTRv3: Spatial and Temporal Context Foster Robust Tracking of Any Point in Long Video
Paper • 2411.18671 • Published • 20 -
Fully Open Source Moxin-7B Technical Report
Paper • 2412.06845 • Published • 11 -
Small Language Models: Survey, Measurements, and Insights
Paper • 2409.15790 • Published • 1 -
Qwen2 Technical Report
Paper • 2407.10671 • Published • 161 -
Phi-4 Technical Report
Paper • 2412.08905 • Published • 106 -
Apollo: An Exploration of Video Understanding in Large Multimodal Models
Paper • 2412.10360 • Published • 139 -
Byte Latent Transformer: Patches Scale Better Than Tokens
Paper • 2412.09871 • Published • 89 -
Qwen2.5 Technical Report
Paper • 2412.15115 • Published • 345 -
Search-o1: Agentic Search-Enhanced Large Reasoning Models
Paper • 2501.05366 • Published • 90 -
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 253 -
MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 272 -
Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
Paper • 2501.09686 • Published • 36 -
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Paper • 2501.12948 • Published • 301 -
SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training
Paper • 2501.17161 • Published • 100 -
Baichuan-Omni-1.5 Technical Report
Paper • 2501.15368 • Published • 53 -
OmniHuman-1: Rethinking the Scaling-Up of One-Stage Conditioned Human Animation Models
Paper • 2502.01061 • Published • 152 -
The Differences Between Direct Alignment Algorithms are a Blur
Paper • 2502.01237 • Published • 105