36 citations · 55 across the 11 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.CL2026
Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline
Tony Lee, Percy Liang
Can post-trained large language models (LLMs) further improve themselves using only unlabeled prompts, without external teachers or feedback from tools? We study this setting start…
cs.RO2026
VLAW: Iterative Co-Improvement of Vision-Language-Action Policy and World Model
Yanjiang Guo, Tony Lee, Lucy Xiaoyang Shi +3
The goal of this paper is to improve the performance and reliability of vision-language-action (VLA) models through iterative online interaction. Since collecting policy rollouts i…
cs.RO2026
RoboReward: General-Purpose Vision-Language Reward Models for Robotics
Tony Lee, Andrew Wagenmaker, Karl Pertsch +3
A well-designed reward is critical for effective reinforcement learning-based policy improvement. In real-world robotics, obtaining such rewards typically requires either labor-int…