Hi, my name is Mingxuan Song.
I'm currently pursuing my PhD at Peking University, under the supervision of Professor Zhen Xiao.

Affiliations:
School of Computer Science, Peking University;
Alimama, Alibaba Group.

In recent years, Large Language Models (LLMs) have demonstrated remarkable performance across a variety of natural language processing tasks. However, fine-tuning these models typically requires large-scale datasets and extensive computational resources, which limits their applicability in scenarios where data is scarce and budgets are constrained. This work explores a novel approach to few-shot reinforcement learning (RL) fine-tuning for LLMs, aiming to adapt pre-trained models to specific tasks using minimal supervision.

Affiliations:
School of Computer Science, Peking University;
Theta Labs, Theta Inc.

Sharding blockchain systems face critical challenges in achieving efficient cross-shard data distribution and maintaining balanced workload across shards. Traditional address allocation methods often suffer from high latency and uneven shard utilization, especially when dealing with dynamically changing transaction patterns and reconfiguration events.

Affiliations:
School of Computer Science, China University of Geosciences, Wuhan.

In water supply networks, effective sensor placement is critical for early detection of contamination events, yet practical deployments are constrained by limited sensor budgets and scarce historical contamination data. This work investigates an evolutionary reinforcement learning formulation of the sensor placement problem, modeling it as a sequential decision-making process under limited supervision. By incorporating domain knowledge into an evolutionary reinforcement learning framework, the proposed approach enables efficient optimization.