2 citations · 3 across the 16 of their papers we have counts for
16 papers
CVPO: Enhancing LLM Reinforcement Learning Reasoning via Value-Variance Adaptation and Dynamic Curriculum Learning
Ziqi Jia, Yalu Ouyang, Bo Pang +5
Reinforcement learning (RL) has emerged as an effective method for enhancing the reasoning capabilities of large language models (LLMs). However, existing methods suffer from insuf…
Harness Engineering for LLM-Driven GPU Kernel Generation
Yue Shui, Chenyu Ma, Hangfei Xu +2
Large language models (LLMs) can assist GPU kernel generation, but their practical effectiveness depends on whether generated code can be reliably constrained, validated, profiled,…
RADAR: Revealing Asymmetric Development of Abilities in MLLM Pre-training
Yunshuang Nie, Bingqian Lin, Minzhe Niu +7
Pre-trained Multi-modal Large Language Models (MLLMs) provide a knowledge-rich foundation for post-training by leveraging their inherent perception and reasoning capabilities to so…
Dependently-Typed AARA: A Non-Affine Approach for Resource Analysis of Higher-Order Programs
Han Xu, Di Wang
Static resource analysis determines the resource consumption (e.g., time complexity) of a program without executing it. Among the numerous existing approaches for resource analysis…
Ensemble Privacy Defense for Knowledge-Intensive LLMs against Membership Inference Attacks
Haowei Fu, Bo Ni, Han Xu +3
Retrieval-Augmented Generation (RAG) and Supervised Finetuning (SFT) have become the predominant paradigms for equipping Large Language Models (LLMs) with external knowledge for di…
VTimeCoT: Thinking by Drawing for Video Temporal Grounding and Reasoning
Jinglei Zhang, Yuanfan Guo, Rolandos Alexandros Potamias +3
In recent years, video question answering based on multimodal large language models (MLLM) has garnered considerable attention, due to the benefits from the substantial advancement…
Towards Proprioception-Aware Embodied Planning for Dual-Arm Humanoid Robots
Boyu Li, Siyuan He, Hang Xu +10
In recent years, Multimodal Large Language Models (MLLMs) have demonstrated the ability to serve as high-level planners, enabling robots to follow complex human instructions. Howev…
RTFF: Random-to-Target Fabric Flattening Policy using Dual-Arm Manipulator
Kai Tang, Dipankar Bhattacharya, Hang Xu +3
Robotic fabric manipulation remains challenging due to fabric deformability and occlusions from wrinkles and the manipulator. This paper defines Random-to-Target Fabric Flattening…
MCTS-EP: Empowering Embodied Planning with Online Preference Optimization
Hang Xu, Zang Yu, Yehui Tang +3
This paper introduces MCTS-EP, an online learning framework that combines large language models (LLM) with Monte Carlo Tree Search (MCTS) for training embodied agents. MCTS-EP inte…
ComputerRL: Scaling End-to-End Online Reinforcement Learning for Computer Use Agents
Hanyu Lai, Xiao Liu, Yanxiao Zhao +7
We introduce ComputerRL, a framework for autonomous desktop intelligence that enables agents to operate complex digital workspaces skillfully. ComputerRL features the API-GUI parad…
FEVO: Financial Knowledge Expansion and Reasoning Evolution for Large Language Models
Bo Pang, Yalu Ouyang, Hangfei Xu +6
Advancements in reasoning for large language models (LLMs) have lead to significant performance improvements for LLMs in various fields such as mathematics and programming. However…
DualTHOR: A Dual-Arm Humanoid Simulation Platform for Contingency-Aware Planning
Boyu Li, Siyuan He, Hang Xu +9
Developing embodied agents capable of performing complex interactive tasks in real-world scenarios remains a fundamental challenge in embodied AI. Although recent advances in simul…
ZO2: Scalable Zeroth-Order Fine-Tuning for Extremely Large Language Models with Limited GPU Memory
Liangyu Wang, Jie Ren, Hang Xu +4
Fine-tuning large pre-trained LLMs generally demands extensive GPU memory. Traditional first-order optimizers like SGD encounter substantial difficulties due to increased memory re…
Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k
Zangwei Zheng, Xiangyu Peng, Yuxuan Lou +30
Video generation models have achieved remarkable progress in the past year. The quality of AI video continues to improve, but at the cost of larger model size, increased data quant…
Certified Robustness for Deep Equilibrium Models via Serialized Random Smoothing
Weizhi Gao, Zhichao Hou, Han Xu +1
Implicit models such as Deep Equilibrium Models (DEQs) have emerged as promising alternative approaches for building deep neural networks. Their certified robustness has gained inc…
Self-playing Adversarial Language Game Enhances LLM Reasoning
Pengyu Cheng, Tianhao Hu, Han Xu +6
We explore the potential of self-play training for large language models (LLMs) in a two-player adversarial language game called Adversarial Taboo. In this game, an attacker and a…