3 citations · 6 across the 20 of their papers we have counts for
24 papers
RoboTacDex: A Dexterous Visual-Tactile-Action Dataset for Humanoid Manipulation
Xinyi Wang, Donghan Li, Zi'Ang Chen +5
In the field of robot learning, large-scale and diverse demonstration trajectories provide the fundamental basis for enhancing robotic manipulation ability. We introduce RoboTacDex…
DiBS: Diffusion-Informed Branch Selection
Bo Liu, Yuan Xie, Yuan Gao +4
Sudoku is a representative constraint satisfaction problem that requires global structural reasoning under strict discrete constraints. The existing works of solving Sudoku mainly…
Learning to Explore with Parameter-Space Noise: A Deep Dive into Parameter-Space Noise for Reinforcement Learning with Verifiable Rewards
Bizhe Bai, Xinyue Wang, Peng Ye +1
Reinforcement Learning with Verifiable Rewards (RLVR) improves LLM reasoning, yet growing evidence indicates an exploration ceiling: it often reweights existing solution traces rat…
FRISM: Fine-Grained Reasoning Injection via Subspace-Level Model Merging for Vision-Language Models
Chenyu Huang, Peng Ye, Xudong Tan +4
Efficiently enhancing the reasoning capabilities of Vision-Language Models (VLMs) by merging them with Large Reasoning Models (LRMs) has emerged as a promising direction. However,…
EGM: Efficiently Learning General Motion Tracking Policy for High Dynamic Humanoid Whole-Body Control
Chao Yang, Yingkai Sun, Peng Ye +3
Learning a general motion tracking policy from human motions shows great potential for versatile humanoid whole-body control. Conventional approaches are not only inefficient in da…
M-GRPO: Stabilizing Self-Supervised Reinforcement Learning for Large Language Models with Momentum-Anchored Policy Optimization
Bizhe Bai, Hongming Wu, Peng Ye +1
Self-supervised reinforcement learning (RL) presents a promising approach for enhancing the reasoning capabilities of Large Language Models (LLMs) without reliance on expensive hum…
Revisiting Multimodal KV Cache Compression: A Frequency-Domain-Guided Outlier-KV-Aware Approach
Yaoxin Yang, Peng Ye, Xudong Tan +4
Multimodal large language models suffer from substantial inference overhead since multimodal KV Cache grows proportionally with the visual input length. Existing multimodal KV Cach…
RegionE: Adaptive Region-Aware Generation for Efficient Image Editing
Pengtao Chen, Xianfang Zeng, Maosen Zhao +7
Recently, instruction-based image editing (IIE) has received widespread attention. In practice, IIE often modifies only specific regions of an image, while the remaining areas larg…
Wisdom of the Crowd: Reinforcement Learning from Coevolutionary Collective Feedback
Wenzhen Yuan, Shengji Tang, Weihao Lin +8
Reinforcement learning (RL) has significantly enhanced the reasoning capabilities of large language models (LLMs), but its reliance on expensive human-labeled data or complex rewar…
Sparse-vDiT: Unleashing the Power of Sparse Attention to Accelerate Video Diffusion Transformers
Pengtao Chen, Xianfang Zeng, Maosen Zhao +5
While Diffusion Transformers (DiTs) have achieved breakthroughs in video generation, this long sequence generation task remains constrained by the quadratic complexity of attention…
PaceLLM: Brain-Inspired Large Language Models for Long-Context Understanding
Kangcong Li, Peng Ye, Chongjun Tu +6
While Large Language Models (LLMs) demonstrate strong performance across domains, their long-context capabilities are limited by transient neural activations causing information de…
Think Twice, Act Once: Token-Aware Compression and Action Reuse for Efficient Inference in Vision-Language-Action Models
Xudong Tan, Yaoxin Yang, Peng Ye +5
Vision-Language-Action (VLA) models have emerged as a powerful paradigm for general-purpose robot control through natural language instructions. However, their high inference cost-…
FAVOR-Bench: A Comprehensive Benchmark for Fine-Grained Video Motion Understanding
Chongjun Tu, Lin Zhang, Pengtao Chen +5
Multimodal Large Language Models (MLLMs) have shown remarkable capabilities in video content understanding but still struggle with fine-grained motion comprehension. To comprehensi…
TokenCarve: Information-Preserving Visual Token Compression in Multimodal Large Language Models
Xudong Tan, Peng Ye, Chongjun Tu +5
Multimodal Large Language Models (MLLMs) are becoming increasingly popular, while the high computational cost associated with multimodal data input, particularly from visual tokens…
Multi-Level Decoupled Relational Distillation for Heterogeneous Architectures
Yaoxin Yang, Peng Ye, Weihao Lin +4
Heterogeneous distillation is an effective way to transfer knowledge from cross-architecture teacher models to student models. However, existing heterogeneous distillation methods…
COMET: Benchmark for Comprehensive Biological Multi-omics Evaluation Tasks and Language Models
Yuchen Ren, Wenwei Han, Qianyuan Zhang +13
As key elements within the central dogma, DNA, RNA, and proteins play crucial roles in maintaining life by guaranteeing accurate genetic expression and implementation. Although res…
All-in-One: Transferring Vision Foundation Models into Stereo Matching
Jingyi Zhou, Haoyu Zhang, Jiakang Yuan +5
As a fundamental vision task, stereo matching has made remarkable progress. While recent iterative optimization-based methods have achieved promising performance, their feature ext…
S2HPruner: Soft-to-Hard Distillation Bridges the Discretization Gap in Pruning
Weihao Lin, Shengji Tang, Chong Yu +2
Recently, differentiable mask pruning methods optimize the continuous relaxation architecture (soft network) as the proxy of the pruned discrete network (hard network) for superior…
HiSplat: Hierarchical 3D Gaussian Splatting for Generalizable Sparse-View Reconstruction
Shengji Tang, Weicai Ye, Peng Ye +4
Reconstructing 3D scenes from multiple viewpoints is a fundamental task in stereo vision. Recently, advances in generalizable 3D Gaussian Splatting have enabled high-quality novel…
DynaSurfGS: Dynamic Surface Reconstruction with Planar-based Gaussian Splatting
Weiwei Cai, Weicai Ye, Peng Ye +2
Dynamic scene reconstruction has garnered significant attention in recent years due to its capabilities in high-quality and real-time rendering. Among various methodologies, constr…
-DiT: A Training-Free Acceleration Method Tailored for Diffusion Transformers
Pengtao Chen, Mingzhu Shen, Peng Ye +5
Diffusion models are widely recognized for generating high-quality and diverse images, but their poor real-time performance has led to numerous acceleration works, primarily focusi…
Once for Both: Single Stage of Importance and Sparsity Search for Vision Transformer Compression
Hancheng Ye, Chong Yu, Peng Ye +5
Recent Vision Transformer Compression (VTC) works mainly follow a two-stage scheme, where the importance score of each model unit is first evaluated or preset in each submodule, fo…
Enhanced Sparsification via Stimulative Training
Shengji Tang, Weihao Lin, Hancheng Ye +4
Sparsification-based pruning has been an important category in model compression. Existing methods commonly set sparsity-inducing penalty terms to suppress the importance of droppe…
MADTP: Multimodal Alignment-Guided Dynamic Token Pruning for Accelerating Vision-Language Transformer
Jianjian Cao, Peng Ye, Shengze Li +4
Vision-Language Transformers (VLTs) have shown great success recently, but are meanwhile accompanied by heavy computation costs, where a major reason can be attributed to the large…