papers

Publications (11)

cs.CL2026

VRM: Teaching Reward Models to Understand Authentic Human Preferences

Biao Liu, Ning Xu, Junming Yang +2

Large Language Models (LLMs) have achieved remarkable success across diverse natural language tasks, yet the reward models employed for aligning LLMs often encounter challenges of…

cs.LG2024

Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning

Qi Wang, Junming Yang, Yunbo Wang +3

Training offline RL models using visual inputs poses two significant challenges, i.e., the overfitting problem in representation learning and the overestimation bias for expected f…

q-fin.CP2019

Deep Learning for Stock Selection Based on High Frequency Price-Volume Data

Junming Yang, Yaoqi Li, Xuanyu Chen +2

Training a practical and effective model for stock selection has been a greatly concerned problem in the field of artificial intelligence. Even though some of the models from previ…

cs.AI2026

SpatialWorld: Benchmarking Interactive Spatial Reasoning of Multimodal Agents in Real-World Tasks

Hongcheng Gao, Hailong Qu, Jingyi Tang +18

Spatial reasoning is a foundational capability for multimodal large language models (MLLMs) to perceive and operate within the physical world. However, existing benchmarks predomin…

cs.CL2026

Alignment through Meta-Weighted Online Sampling: Bridging the Gap between Data Generation and Preference Optimization

Junming Yang, Ning Xu, Biao Liu +2

Preference optimization is crucial for aligning large language models (LLMs) with human values and intentions. A significant challenge in this process is the distribution mismatch…

cs.CV2024

Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs

Yuxuan Qiao, Haodong Duan, Xinyu Fang +6

Vision Language Models (VLMs) demonstrate remarkable proficiency in addressing a wide array of visual questions, which requires strong perception and reasoning faculties. Assessing…

cs.CV2024

CoCoT: Contrastive Chain-of-Thought Prompting for Large Multimodal Models with Multiple Image Inputs

Daoan Zhang, Junming Yang, Hanjia Lyu +4

When exploring the development of Artificial General Intelligence (AGI), a critical task for these models involves interpreting and processing information from multiple image input…

cs.CV2026

VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models

Haodong Duan, Xinyu Fang, Junming Yang +41

We present VLMEvalKit: an open-source toolkit for evaluating large multi-modality models based on PyTorch. The toolkit aims to provide a user-friendly and comprehensive framework f…

cs.CV2025

Extracting Multimodal Learngene in CLIP: Unveiling the Multimodal Generalizable Knowledge

Ruiming Chen, Junming Yang, Shiyu Xia +3

CLIP (Contrastive Language-Image Pre-training) has attracted widespread attention for its multimodal generalizable knowledge, which is significant for downstream tasks. However, th…

cs.LG2023

Model-based Offline Policy Optimization with Adversarial Network

Junming Yang, Xingguo Chen, Shengyuan Wang +1

Model-based offline reinforcement learning (RL), which builds a supervised transition model with logging dataset to avoid costly interactions with the online environment, has been…

cs.CL2025

Preference Orchestrator: Prompt-Aware Multi-Objective Alignment for Large Language Models

Biao Liu, Ning Xu, Junming Yang +1

While Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks, aligning these models with varying human preferences…