25 citations · 26 across the 8 of their papers we have counts for
9 papers
Embracing Flow Unsteadiness: A High-Throughput Learning Platform Enables Vortex-Exploiting Bioinspired Propulsion
Fei Han, Xinyu Cui, Zhipeng Wang +7
Biological swimmers and flyers exploit unsteady vortices for propulsion, whereas engineered vehicles usually suppress them as disturbances. Learning such flow exploitation in machi…
SRPO: Self-Reflective Policy Optimization for Long-Horizon Reasoning
Jialong Liu, Yuling Shi, Ning Yang +2
Self-reflection is a powerful mechanism for credit assignment in human learning, converting sparse outcome feedback into actionable guidance. However, its potential for post-traini…
SF-AMS: Strategic Forgetting for Structured Memory in LLM Agent
Ning Yang, Siqi Li, Miaoxin Shen +4
Managing long-context dependencies remains a primary bottleneck in LLM agents, as redundant and irrelevant information can degrade multi-step reasoning. Strategic Forgetting for Ag…
LinearARD: Linear-Memory Attention Distillation for RoPE Restoration
Ning Yang, Hengyu Zhong, Wentao Wang +4
The extension of context windows in Large Language Models is typically facilitated by scaling positional encodings followed by Continued Pre-Training (CPT). While effective, this p…
Proactive Constrained Policy Optimization with Preemptive Penalty
Ning Yang, Pengyu Wang, Guoqing Liu +3
Safe Reinforcement Learning (RL) often faces significant issues such as constraint violations and instability, necessitating the use of constrained policy optimization, which seeks…
Enhancing Efficiency and Propulsion in Bio-mimetic Robotic Fish through End-to-End Deep Reinforcement Learning
Xinyu Cui, Boai Sun, Yi Zhu +5
Aquatic organisms are known for their ability to generate efficient propulsion with low energy expenditure. While existing research has sought to leverage bio-inspired structures t…
Evolving LLMs' Self-Refinement Capability via Synergistic Training-Inference Optimization
Yongcheng Zeng, Xinyu Cui, Xuanfa Jin +11
Self-Refinement refers to a model's ability to revise its own responses to produce improved outputs. This capability can also serve as a fundamental mechanism for Self-Improvement,…
Token-level Direct Preference Optimization
Yongcheng Zeng, Guoqing Liu, Weiyu Ma +3
Fine-tuning pre-trained Large Language Models (LLMs) is essential to align them with human values and intentions. This process often utilizes methods like pairwise comparisons and…
Mean Field Correlated Imitation Learning
Zhiyu Zhao, Qirui Mi, Ning Yang +4
We investigate multi-agent imitation learning (IL) within the framework of mean field games (MFGs), considering the presence of time-varying correlated signals. Existing MFG IL alg…