1 citations · 1 across the 6 of their papers we have counts for
7 papers
CoRDE: Concept-Prior Routed Diffusion Experts for Structural Generalization in Robot Manipulation
Haidong Huang, Xixin Zhao, Yaohua Zhou +5
Diffusion models excel at capturing multi-modal action distributions in robot imitation learning. However, in multi-task and long-horizon scenarios, monolithic architectures lack s…
From Shallow to Deep: Pinning Semantic Intent via Causal GRPO
Shuyi Zhou, Zeen Song, Wenwen Qiang +4
Large Language Models remain vulnerable to adversarial prefix attacks (e.g., ``Sure, here is'') despite robust standard safety. We diagnose this vulnerability as Shallow Safety Ali…
Opinion: Towards Unified Expressive Policy Optimization for Robust Robot Learning
Haidong Huang, Haiyue Zhu. Jiayu Song, Xixin Zhao +4
Offline-to-online reinforcement learning (O2O-RL) has emerged as a promising paradigm for safe and efficient robotic policy deployment but suffers from two fundamental challenges:…
Reclaiming Low-Quality Instruction Data with Rule-Guided LLM Reconstruction
Zile Yang, Ling Li, Na Di +5
Data selection for supervised fine-tuning (SFT) often discards low-scoring instruction examples that may retain useful supervision. We propose a rule-guided LLM reconstruction fram…
LM-mixup: Text Data Augmentation via Language Model based Mixup
Zhijie Deng, Zhouan Shen, Ling Li +5
Instruction tuning is crucial for aligning Large Language Models (LLMs), yet the quality of instruction-following data varies significantly. While high-quality data is paramount, i…
Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models
Ling Li, Yao Zhou, Yuxuan Liang +2
Previous methods for image geo-localization have typically treated the task as either classification or retrieval, often relying on black-box decisions that lack interpretability.…
GeoReasoner: Geo-localization with Reasoning in Street Views using a Large Vision-Language Model
Ling Li, Yu Ye, Yao Zhou +2
This work tackles the problem of geo-localization with a new paradigm using a large vision-language model (LVLM) augmented with human inference knowledge. A primary challenge here…