1 citations · 1 across the 6 of their papers we have counts for
8 papers
Hi-WM: Human-in-the-World-Model for Scalable Robot Post-Training
Yaxuan Li, Zhongyi Zhou, Yefei Chen +5
Post-training is essential for turning pretrained generalist robot policies into reliable task-specific controllers, but existing human-in-the-loop pipelines remain tied to physica…
dWorldEval: Scalable Robotic Policy Evaluation via Discrete Diffusion World Model
Yaxuan Li, Zhongyi Zhou, Yefei Chen +2
Evaluating robotics policies across thousands of environments and thousands of tasks is infeasible with existing approaches. This motivates the need for a new methodology for scala…
Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe
Yaxuan Li, Yuxin Zuo, Bingxiang He +8
On-policy distillation (OPD) has become a core technique in the post-training of large language models, yet its training dynamics remain poorly understood. This paper provides a sy…
Towards High-Consistency Embodied World Model with Multi-View Trajectory Videos
Taiyi Su, Jian Zhu, Yaxuan Li +5
Embodied world models aim to predict and interact with the physical world through visual observations and actions. However, existing models struggle to accurately translate low-lev…
DeepPrune: Parallel Scaling without Inter-trace Redundancy
Shangqing Tu, Yaxuan Li, Yushi Bai +2
Parallel scaling has emerged as a powerful paradigm to enhance reasoning capabilities in large language models (LLMs) by generating multiple Chain-of-Thought (CoT) traces simultane…
DialogueForge: LLM Simulation of Human-Chatbot Dialogue
Ruizhe Zhu, Hao Zhu, Yaxuan Li +4
Collecting human-chatbot dialogues typically demands substantial manual effort and is time-consuming, which limits and poses challenges for research on conversational AI. In this w…
WorldEval: World Model as Real-World Robot Policies Evaluator
Yaxuan Li, Yichen Zhu, Junjie Wen +2
The field of robotics has made significant strides toward developing generalist robot manipulation policies. However, evaluating these policies in real-world scenarios remains time…
DAIL: Data Augmentation for In-Context Learning via Self-Paraphrase
Dawei Li, Yaxuan Li, Dheeraj Mekala +5
In-Context Learning (ICL) combined with pre-trained large language models has achieved promising results on various NLP tasks. However, ICL requires high-quality annotated demonstr…