Summary of Ompo: a Unified Framework For Rl Under Policy and Dynamics Shifts, by Yu Luo et al.
OMPO: A Unified Framework for RL under Policy and Dynamics Shiftsby Yu Luo, Tianying Ji,…
OMPO: A Unified Framework for RL under Policy and Dynamics Shiftsby Yu Luo, Tianying Ji,…
Can Graph Learning Improve Planning in LLM-based Agents?by Xixi Wu, Yifei Shen, Caihua Shan, Kaitao…
A Study of Plasticity Loss in On-Policy Deep Reinforcement Learningby Arthur Juliani, Jordan T. AshFirst…
Does learning the right latent variables necessarily improve in-context learning?by Sarthak Mittal, Eric Elmoznino, Leo…
UniIF: Unified Molecule Inverse Foldingby Zhangyang Gao, Jue Wang, Cheng Tan, Lirong Wu, Yufei Huang,…
State Space Models are Provably Comparable to Transformers in Dynamic Token Selectionby Naoki Nishikawa, Taiji…
LSPI: Heterogeneous Graph Neural Network Classification Aggregation Algorithm Based on Size Neighbor Path Identificationby Yufei…
On the Role of Attention Masks and LayerNorm in Transformersby Xinyi Wu, Amir Ajorlou, Yifei…
AnyFit: Controllable Virtual Try-on for Any Combination of Attire Across Any Scenarioby Yuhan Li, Hao…
In-Context Symmetries: Self-Supervised Learning through Contextual World Modelsby Sharut Gupta, Chenyu Wang, Yifei Wang, Tommi…