Revisiting Multimodal Positional Encoding in Vision-Language Models Paper • 2510.23095 • Published 13 days ago • 18
QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs Paper • 2510.11696 • Published 27 days ago • 173
Training-Free Group Relative Policy Optimization Paper • 2510.08191 • Published about 1 month ago • 44
Learn the Ropes, Then Trust the Wins: Self-imitation with Progressive Exploration for Agentic Reinforcement Learning Paper • 2509.22601 • Published Sep 26 • 29
LongLive: Real-time Interactive Long Video Generation Paper • 2509.22622 • Published Sep 26 • 181
Scalable Multi-Task Reinforcement Learning for Generalizable Spatial Intelligence in Visuomotor Agents Paper • 2507.23698 • Published Jul 31 • 9
Scalable Multi-Task Reinforcement Learning for Generalizable Spatial Intelligence in Visuomotor Agents Paper • 2507.23698 • Published Jul 31 • 9 • 4
Scalable Multi-Task Reinforcement Learning for Generalizable Spatial Intelligence in Visuomotor Agents Paper • 2507.23698 • Published Jul 31 • 9 • 4
ROCKET Collection ROCKET is the research series that explores vision-based goal specification methods. • 12 items • Updated Sep 21 • 2
Scalable Multi-Task Reinforcement Learning for Generalizable Spatial Intelligence in Visuomotor Agents Paper • 2507.23698 • Published Jul 31 • 9
Scalable Multi-Task Reinforcement Learning for Generalizable Spatial Intelligence in Visuomotor Agents Paper • 2507.23698 • Published Jul 31 • 9 • 4
Open-World Skill Discovery from Unsegmented Demonstrations Paper • 2503.10684 • Published Mar 11 • 5
A Survey on Vision-Language-Action Models: An Action Tokenization Perspective Paper • 2507.01925 • Published Jul 2 • 38
A Survey on Vision-Language-Action Models: An Action Tokenization Perspective Paper • 2507.01925 • Published Jul 2 • 38
GROOT Collection GROOT is a research series investigating how self-supervised and weakly supervised learning can be used to train agents that follow instructions. • 3 items • Updated Aug 3 • 2
GROOT-2: Weakly Supervised Multi-Modal Instruction Following Agents Paper • 2412.10410 • Published Dec 7, 2024
GROOT Collection GROOT is a research series investigating how self-supervised and weakly supervised learning can be used to train agents that follow instructions. • 3 items • Updated Aug 3 • 2
Generative Evaluation of Complex Reasoning in Large Language Models Paper • 2504.02810 • Published Apr 3 • 14
ROCKET-2: Steering Visuomotor Policy via Cross-View Goal Alignment Paper • 2503.02505 • Published Mar 4 • 7 • 10