2 citations · 2 across the 5 of their papers we have counts for
6 papers
LLM-as-a-Verifier: A General-Purpose Verification Framework
Jacky Kwok, Shulu Li, Pranav Atreya +6
Scaling pre-training, post-training, and test-time compute have become the central paradigms for improving the capabilities of LLMs. In this work, we identify verification, the abi…
Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment
Jacky Kwok, Xilun Zhang, Mengdi Xu +4
The long-standing vision of general-purpose robots hinges on their ability to understand and act upon natural language instructions. Vision-Language-Action (VLA) models have made r…
RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models
Jacky Kwok, Christopher Agia, Rohan Sinha +5
Vision-Language-Action (VLA) models have demonstrated remarkable capabilities in visuomotor control, yet ensuring their robustness in unstructured real-world environments remains a…
HPRM: High-Performance Robotic Middleware for Intelligent Autonomous Systems
Jacky Kwok, Shulu Li, Marten Lohstroh +1
The rise of intelligent autonomous systems, especially in robotics and autonomous agents, has created a critical need for robust communication middleware that can ensure real-time…
SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning
Yizhou Chi, Yizhang Lin, Sirui Hong +9
Automated Machine Learning (AutoML) approaches encompass traditional methods that optimize fixed pipelines for model selection and ensembling, as well as newer LLM-based frameworks…
Efficient Parallel Reinforcement Learning Framework using the Reactor Model
Jacky Kwok, Marten Lohstroh, Edward A. Lee
Parallel Reinforcement Learning (RL) frameworks are essential for mapping RL workloads to multiple computational resources, allowing for faster generation of samples, estimation of…