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RL Zero: Zero-Shot Language to Behaviors without any Supervision
Paper • 2412.05718 • Published • 4 -
Offline Reinforcement Learning for LLM Multi-Step Reasoning
Paper • 2412.16145 • Published • 38 -
Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning
Paper • 2412.15797 • Published • 17 -
Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search
Paper • 2412.18319 • Published • 37
Collections
Discover the best community collections!
Collections including paper arxiv:2501.03575
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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 23 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 83 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 146 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
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Cosmos World Foundation Model Platform for Physical AI
Paper • 2501.03575 • Published • 68 -
VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM
Paper • 2501.00599 • Published • 41 -
OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints
Paper • 2501.03841 • Published • 53 -
Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives
Paper • 2501.04003 • Published • 25
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Cosmos World Foundation Model Platform for Physical AI
Paper • 2501.03575 • Published • 68 -
Phi-4 Technical Report
Paper • 2412.08905 • Published • 106 -
MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 272 -
DeepSeek-V3 Technical Report
Paper • 2412.19437 • Published • 47
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MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models
Paper • 2501.02955 • Published • 40 -
2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining
Paper • 2501.00958 • Published • 99 -
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
Paper • 2501.12380 • Published • 81 -
VideoWorld: Exploring Knowledge Learning from Unlabeled Videos
Paper • 2501.09781 • Published • 24