7 papers
MAGNIFIED: RL Fine-tuning of Multimodal Large Language Models for Motion Planning
Letian Chen, Yiren Lu, Justin Fu +5
Multi-modal Large Language Models (MLLMs) have demonstrated remarkable capabilities in semantic understanding and common sense reasoning, making them promising candidates for solvi…
PulseCol: Periodically Refreshed Column-Sparse Attention for Accelerating Diffusion Language Models
Yanyi Lyu, Letian Chen, Futing Sun +3
Inference in diffusion large language models (dLLMs) is computationally expensive, as full self-attention must be repeatedly executed at each step of the denoising process without…
FlowLM: Few-Step Language Modeling via Diffusion-to-Flow Adaptation
Runzhe Zhang, Letian Chen, Wenpeng Zhang +2
We present FlowLM, a flow matching language model transformed from pre-trained diffusion language models via efficient fine-tuning. By re-aligning the curved sampling trajectories…
Towards Automated Semantic Interpretability in Reinforcement Learning via Vision-Language Models
Zhaoxin Li, Zhang Xi-Jia, Batuhan Altundas +3
Semantic interpretability in Reinforcement Learning (RL) enables transparency and verifiability of decision-making. Achieving semantic interpretability in reinforcement learning re…
Faster Model Predictive Control via Self-Supervised Initialization Learning
Zhaoxin Li, Xiaoke Wang, Letian Chen +3
Model Predictive Control (MPC) is widely used in robot control by optimizing a sequence of control outputs over a finite-horizon. Computational approaches for MPC include determini…
Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations
Letian Chen, Sravan Jayanthi, Rohan Paleja +3
Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics. However, c…