1 citations · 1 across the 8 of their papers we have counts for
9 papers
Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedback
Li Wang, Chen Zhang, Yirong Yang +4
User feedback enables LLMs to refine reusable personalized skills, but often mixes persistent preferences with request-specific corrections. Indiscriminately retaining both risks n…
VoLN: Vision-Only Long-Horizon Navigation---Paradigm, Benchmark, and Method
Jiabin Lou, Haopeng Wang, Yuanshuai Wang +6
Vision-and-Language Navigation (VLN) enables embodied agents to follow natural-language instructions. However, route-level instructions commonly encode spatial priors, such as orie…
AERIS: Aerial-Edge Role-Driven Intelligence at Runtime via Orchestrated Language-Model Swarm
Jiabin Lou, Haopeng Wang, Xinyu Liu +3
Integrating large language models into robotic systems holds promise for enhancing autonomy, yet practical deployment remains constrained by strict heartbeat-constrained scheduling…
FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning
Xikai Zhang, Yongzhi Li, Likang Xiao +6
Reinforcement learning has become a cornerstone for aligning and unlocking the reasoning capabilities of large-scale models. At its core, the training loop of GRPO and its variants…
Learning to Adapt SFT Data for Better Reasoning Generalization
Lisong Sun, Li Wang, Chen Zhang +4
Large language models (LLMs) have achieved remarkable progress, with post-training playing a crucial role in enhancing their reasoning capabilities. Among post-training paradigms,…
Symmetry-Guided Multi-Agent Inverse Reinforcement Learning
Yongkai Tian, Yirong Qi, Xin Yu +2
In robotic systems, the performance of reinforcement learning depends on the rationality of predefined reward functions. However, manually designed reward functions often lead to p…
Neural Algorithmic Reasoners informed Large Language Model for Multi-Agent Path Finding
Pu Feng, Size Wang, Yuhong Cao +3
The development and application of large language models (LLM) have demonstrated that foundational models can be utilized to solve a wide array of tasks. However, their performance…
Hierarchical Consensus-Based Multi-Agent Reinforcement Learning for Multi-Robot Cooperation Tasks
Pu Feng, Junkang Liang, Size Wang +6
In multi-agent reinforcement learning (MARL), the Centralized Training with Decentralized Execution (CTDE) framework is pivotal but struggles due to a gap: global state guidance in…
Leveraging Partial Symmetry for Multi-Agent Reinforcement Learning
Xin Yu, Rongye Shi, Pu Feng +4
Incorporating symmetry as an inductive bias into multi-agent reinforcement learning (MARL) has led to improvements in generalization, data efficiency, and physical consistency. Whi…