G-CUT3R: Guided 3D Reconstruction with Camera and Depth Prior Integration Paper • 2508.11379 • Published Aug 15 • 12
Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success Paper • 2508.04280 • Published Aug 6 • 35
Train Sparse Autoencoders Efficiently by Utilizing Features Correlation Paper • 2505.22255 • Published May 28 • 24
You Do Not Fully Utilize Transformer's Representation Capacity Paper • 2502.09245 • Published Feb 13 • 37
Analyze Feature Flow to Enhance Interpretation and Steering in Language Models Paper • 2502.03032 • Published Feb 5 • 60
The Differences Between Direct Alignment Algorithms are a Blur Paper • 2502.01237 • Published Feb 3 • 113
XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning Paper • 2406.08973 • Published Jun 13, 2024 • 89
Linear Transformers with Learnable Kernel Functions are Better In-Context Models Paper • 2402.10644 • Published Feb 16, 2024 • 81