5 papers
Demystifying Reinforcement Learning Post-Training of Language Models
Donovan Clay, Saket Gollapudi, Sankar Harilal +4
Reinforcement learning (RL) post-training has emerged as a powerful framework for enhancing the capabilities of large language models (LLMs), enabling impressive reasoning, math, a…
MolmoAct2: Action Reasoning Models for Real-world Deployment
Haoquan Fang, Jiafei Duan, Donovan Clay +26
Vision-Language-Action (VLA) models aim to provide a single generalist controller for robots, but today's systems fall short on the criteria that matter for real-world deployment.…
WildDet3D: Scaling Promptable 3D Detection in the Wild
Weikai Huang, Jieyu Zhang, Sijun Li +15
Understanding objects in 3D from a single image is a cornerstone of spatial intelligence. A key step toward this goal is monocular 3D object detection--recovering the extent, locat…
MolmoSpaces: A Large-Scale Open Ecosystem for Robot Navigation and Manipulation
Yejin Kim, Wilbert Pumacay, Omar Rayyan +23
Deploying robots at scale demands robustness to the long tail of everyday situations. The countless variations in scene layout, object geometry, and task specifications that charac…
Consistently Simulating Human Personas with Multi-Turn Reinforcement Learning
Marwa Abdulhai, Ryan Cheng, Donovan Clay +3
Large Language Models (LLMs) are increasingly used to simulate human users in interactive settings such as therapy, education, and social role-play. While these simulations enable…