5 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…
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…
Hierarchical-Task-Aware Multi-modal Mixture of Incremental LoRA Experts for Embodied Continual Learning
Ziqi Jia, Anmin Wang, Xiaoyang Qu +2
Previous continual learning setups for embodied intelligence focused on executing low-level actions based on human commands, neglecting the ability to learn high-level planning and…
Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy
Ziqi Jia, Junjie Li, Xiaoyang Qu +1
Multi-agent systems (MAS) have shown great potential in executing complex tasks, but coordination and safety remain significant challenges. Multi-Agent Reinforcement Learning (MARL…
Incremental Label Distribution Learning with Scalable Graph Convolutional Networks
Ziqi Jia, Xiaoyang Qu, Chenghao Liu +1
Label Distribution Learning (LDL) is an effective approach for handling label ambiguity, as it can analyze all labels at once and indicate the extent to which each label describes…