12 citations · 22 across the 14 of their papers we have counts for
14 papers
Co-RL: Unsupervised Reasoning Emerges from Diverse Cohort in Multi-agent RL
Yunhao Yang, Yuexin Bian, Yunjie Tian +6
Reinforcement learning (RL) has emerged as a powerful approach for improving reasoning in language and vision-language models, yet its strongest successes still depend heavily on g…
Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning
Yuan Zhuang, Yuexin Bian, Sihong He +7
Scaling critic capacity is a promising direction for improving off-policy reinforcement learning (RL). However, recent work shows that larger critics are prone to overfitting and i…
RN-D: Discretized Categorical Actors for On-Policy Reinforcement Learning
Yuexin Bian, Jie Feng, Tao Wang +3
On-policy Reinforcement Learning (RL) remains a dominant paradigm for continuous control, yet standard implementations rely on Gaussian actors and relatively shallow MLP policies,…
LD-MoLE: Learnable Dynamic Routing for Mixture of LoRA Experts
Yuan Zhuang, Yi Shen, Yuexin Bian +4
Recent studies have shown that combining parameter-efficient fine-tuning (PEFT) with mixture-of-experts (MoE) is an effective strategy for adapting large language models (LLMs) to…
Revisiting Reinforcement Learning for LLM Reasoning from A Cross-Domain Perspective
Zhoujun Cheng, Shibo Hao, Tianyang Liu +21
Reinforcement learning (RL) has emerged as a promising approach to improve large language model (LLM) reasoning, yet most open efforts focus narrowly on math and code, limiting our…
Operator learning for energy-efficient building ventilation control with computational fluid dynamics simulation of a real-world classroom
Yuexin Bian, Oliver Schmidt, Yuanyuan Shi
Energy-efficient ventilation control plays a vital role in reducing building energy consumption while ensuring occupant health and comfort. While Computational Fluid Dynamics (CFD)…
DiffOP: Reinforcement Learning of Optimization-Based Control Policies via Implicit Policy Gradients
Yuexin Bian, Jie Feng, Yuanyuan Shi
Real-world control systems require policies that are not only high-performing but also interpretable and robust. A promising direction toward this goal is model-based control, whic…
PDE Control Gym: A Benchmark for Data-Driven Boundary Control of Partial Differential Equations
Luke Bhan, Yuexin Bian, Miroslav Krstic +1
Over the last decade, data-driven methods have surged in popularity, emerging as valuable tools for control theory. As such, neural network approximations of control feedback laws,…
Improving Sequential Market Coordination via Value-oriented Renewable Energy Forecasting
Yufan Zhang, Honglin Wen, Yuexin Bian +1
Large penetration of renewable energy sources (RESs) brings huge uncertainty into the electricity markets. The current deterministic clearing approach in the day-ahead (DA) market,…
Ventilation and Temperature Control for Energy-efficient and Healthy Buildings: A Differentiable PDE Approach
Yuexin Bian, Xiaohan Fu, Rajesh K. Gupta +1
In this paper, we introduce a novel framework for building learning and control, focusing on ventilation and thermal management to enhance energy efficiency. We validate the perfor…
Deriving Loss Function for Value-oriented Renewable Energy Forecasting
Yufan Zhang, Honglin Wen, Yuexin Bian +1
Renewable energy forecasting is the workhorse for efficient energy dispatch. However, forecasts with small mean squared errors (MSE) may not necessarily lead to low operation costs…
Toward Value-oriented Renewable Energy Forecasting: An Iterative Learning Approach
Yufan Zhang, Mengshuo Jia, Honglin Wen +2
Energy forecasting is an essential task in power system operations. Operators usually issue forecasts and leverage them to schedule energy dispatch ahead of time. However, forecast…
Predicting Strategic Energy Storage Behaviors
Yuexin Bian, Ningkun Zheng, Yang Zheng +2
Energy storage are strategic participants in electricity markets to arbitrage price differences. Future power system operators must understand and predict strategic storage arbitra…
Carbon-Aware EV Charging
Kai-Wen Cheng, Yuexin Bian, Yuanyuan Shi +1
This paper examines the problem of optimizing the charging pattern of electric vehicles (EV) by taking real-time electricity grid carbon intensity into consideration. The objective…