12 papers
AC-VLA: Robust Out-of-Distribution Action Execution via Compositional Learning
Xiaojiang Peng, Kai Peng, Jie Lu +3
Vision-Language-Action (VLA) models excel at end-to-end robotic manipulation but struggle with out-of-distribution (OOD) generalization when familiar sub-tasks are recombined in un…
OmniOPSD: Rationale-Privileged On-Policy Self-Distillation for Affective Computing
Zebang Cheng, Shuimu Chen, Boxue Yang +7
Reinforcement learning for multimodal large language models (MLLMs) is often hindered by severe reward sparsity in complex reasoning tasks. This challenge is particularly pronounce…
Unleashing More Actions via Action Compositional Training for VLA Models
Kai Peng, Jie Lu, Xiaojiang Peng
Vision-Language-Action models excel at robotic manipulation, driven by the scale and diversity of demonstration data. However, standard training paradigms often cause VLA models to…
Adaptive Action Chunking at Inference-time for Vision-Language-Action Models
Yuanchang Liang, Xiaobo Wang, Kai Wang +5
In Vision-Language-Action (VLA) models, action chunking (i.e., executing a sequence of actions without intermediate replanning) is a key technique to improve robotic manipulation a…
ViGoR-Bench: How Far Are Visual Generative Models From Zero-Shot Visual Reasoners?
Haonan Han, Jiancheng Huang, Xiaopeng Sun +7
Beneath the stunning visual fidelity of modern AIGC models lies a "logical desert", where systems fail tasks that require physical, causal, or complex spatial reasoning. Current ev…
ResearchGPT: Benchmarking and Training LLMs for End-to-End Computer Science Research Workflows
Penghao Wang, Yuhao Zhou, Mengxuan Wu +12
As large language models (LLMs) advance, the ultimate vision for their role in science is emerging: we could build an AI collaborator to effectively assist human beings throughout…