LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters! Paper • 2502.07374 • Published 3 days ago • 27
MiniMax-01: Scaling Foundation Models with Lightning Attention Paper • 2501.08313 • Published about 1 month ago • 273
The Lessons of Developing Process Reward Models in Mathematical Reasoning Paper • 2501.07301 • Published Jan 13 • 90
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking Paper • 2501.04519 • Published Jan 8 • 255
Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey Paper • 2412.18619 • Published Dec 16, 2024 • 55
ProcessBench: Identifying Process Errors in Mathematical Reasoning Paper • 2412.06559 • Published Dec 9, 2024 • 80
JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation Paper • 2411.07975 • Published Nov 12, 2024 • 30
LLM-based Optimization of Compound AI Systems: A Survey Paper • 2410.16392 • Published Oct 21, 2024 • 14
Qwen2.5 Collection Qwen2.5 language models, including pretrained and instruction-tuned models of 7 sizes, including 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B. • 45 items • Updated Nov 28, 2024 • 517
view article Article Meet Yi-Coder: A Small but Mighty LLM for Code By lorinma • Sep 4, 2024 • 15
view article Article Understanding Vector Quantization in VQ-VAE By ariG23498 • Aug 28, 2024 • 17
Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming Paper • 2408.16725 • Published Aug 29, 2024 • 53