rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset Paper • 2505.21297 • Published May 27 • 30 • 5
rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset Paper • 2505.21297 • Published May 27 • 30
rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset Paper • 2505.21297 • Published May 27 • 30 • 5
rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset Paper • 2505.21297 • Published May 27 • 30
rStar-Coder: Scaling Competitive Code Reasoning with a Large-Scale Verified Dataset Paper • 2505.21297 • Published May 27 • 30 • 5
Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs Paper • 2503.01743 • Published Mar 3 • 88
Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs Paper • 2503.01743 • Published Mar 3 • 88
Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimization Paper • 2502.04295 • Published Feb 6 • 13
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking Paper • 2501.04519 • Published Jan 8 • 283 • 43
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking Paper • 2501.04519 • Published Jan 8 • 283 • 43
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking Paper • 2501.04519 • Published Jan 8 • 283 • 43
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking Paper • 2501.04519 • Published Jan 8 • 283 • 43