1 citations · 1 across the 17 of their papers we have counts for
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Semantic DLM+: Improving Diffusion Language Models through Bias-variance Trade-off in Transition Kernel Design
Keyue Jiang, Yuxiang Wang, Yanan Zhao +7
Diffusion Language Models (DLMs) have demonstrated strong scaling capacity as alternatives to autoregressive language models. However, their performance is highly sensitive to the…
Scalable Distributed Learning-Based MPC for Heterogeneous Consumer-Prosumer Building Aggregations: MPC-Aware Feature Selection and Convex Constraint-Coupled Optimization
Kaipeng Xu, Zhuo Zhi, Keyue Jiang
Buildings provide demand-response flexibility through controllable thermal loads and thermal inertia, while distributed photovoltaic deployment creates opportunities to share surpl…
GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models
Xiaohang Tang, Keyue Jiang, Che Liu +4
Reinforcement learning (RL) can be used to improve the policy (denoiser) of diffusion large language models (dLLMs), while being hindered by the intractability of the policy likeli…
On the Trainability of Masked Diffusion Language Models via Blockwise Locality
Yuxiang Wang, Yu Xiang, Baojian Zhou +4
Masked diffusion language models (MDMs) have recently emerged as a promising alternative to standard autoregressive large language models (AR-LLMs), yet their optimization can be s…
End-to-End Differentiable Predictive Control with Probabilistic Constraint-Satisfaction Guarantees for Building Demand Response
Kaipeng Xu, Zhuo Zhi, Ruixuan Zhao +1
Building Demand Response (DR) benefits from Model Predictive Control (MPC) because thermal flexibility can be optimized over a future horizon while enforcing operational constraint…
Input Convex Encoder-Only Transformer for Computationally Efficient Model Predictive Control in Building Demand Response
Kaipeng Xu, Zhuo Zhi, Keyue Jiang
Learning-based Model Predictive Control (MPC) has emerged as a powerful strategy for building demand response (DR). However, its practical deployment is often hindered by the non-c…
Impact of Connectivity on Laplacian Representations in Reinforcement Learning
Tommaso Giorgi, Pierriccardo Olivieri, Keyue Jiang +2
Learning compact state representations in Markov Decision Processes (MDPs) has proven crucial for addressing the curse of dimensionality in large-scale reinforcement learning (RL)…
Logics-STEM: Empowering LLM Reasoning via Failure-Driven Post-Training and Document Knowledge Enhancement
Mingyu Xu, Cheng Fang, Keyue Jiang +16
We present Logics-STEM, a state-of-the-art reasoning model fine-tuned on Logics-STEM-SFT-Dataset, a high-quality and diverse dataset at 10M scale that represents one of the largest…