Unified Multimodal Understanding and Generation Models: Advances, Challenges, and Opportunities
Paper
• 2505.02567 • Published
• 80
Papers
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TabSTAR: A Foundation Tabular Model With Semantically Target-Aware Representations
Paper • 2505.18125 • Published • 112 -
Distilling LLM Agent into Small Models with Retrieval and Code Tools
Paper • 2505.17612 • Published • 81 -
One RL to See Them All: Visual Triple Unified Reinforcement Learning
Paper • 2505.18129 • Published • 62 -
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
Paper • 2506.13585 • Published • 273 -
Scaling Test-time Compute for LLM Agents
Paper • 2506.12928 • Published • 63 -
Reinforcement Pre-Training
Paper • 2506.08007 • Published • 263 -
Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning
Paper • 2506.07044 • Published • 113 -
ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning
Paper • 2506.09513 • Published • 102 -
Confidence Is All You Need: Few-Shot RL Fine-Tuning of Language Models
Paper • 2506.06395 • Published • 133 -
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 277