4 citations · 5 across the 4 of their papers we have counts for
6 papers
ZOIF: Zeroth-Order Influence Function Estimation for LLM Data Selection Using Only Forward Passes
Lisong He, Yunxuan Jiang, Haishan Ye +1
Influence functions enable principled data selection for large language models (e.g., identifying influential training samples for model editing, debugging, or efficient fine-tunin…
Divide-and-Remember: Recursive Action-Relevant Memory for Long-Horizon VLA Policies
Xuehui Yu, Eason Yu, Meiyi Wang +3
Vision-language-action (VLA) models struggle on history-dependent manipulation tasks, where the current observation alone does not determine the action, and the policy needs a memo…
Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics
Leonard Hinckeldey, Elliot Fosong, Rimvydas Rubavicius +6
As embodied autonomous systems capable of assisting humans in daily activities remain a major goal for robotics, efficient and appropriate reinforcement learning (RL) simulation te…
LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots
Dongge Han, Trevor McInroe, Adam Jelley +3
Large language models (LLMs) have shown significant potential for robotics applications, particularly task planning, by harnessing their language comprehension and text generation…
Multi-view Disentanglement for Reinforcement Learning with Multiple Cameras
Mhairi Dunion, Stefano V. Albrecht
The performance of image-based Reinforcement Learning (RL) agents can vary depending on the position of the camera used to capture the images. Training on multiple cameras simultan…
Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning
Trevor McInroe, Adam Jelley, Stefano V. Albrecht +1
Offline pretraining with a static dataset followed by online fine-tuning (offline-to-online, or OtO) is a paradigm well matched to a real-world RL deployment process. In this scena…