6 citations · 7 across the 10 of their papers we have counts for
10 papers
PrAg-PO: Prompt Augmented Policy Optimization for Robust and Diverse Mathematical Reasoning
Wenquan Lu, Hai Huang, Enqi Liu +1
Reinforcement learning algorithms such as group-relative policy optimization (GRPO) have shown strong potential for improving the mathematical reasoning capabilities of large langu…
Opinion: Towards Unified Expressive Policy Optimization for Robust Robot Learning
Haidong Huang, Haiyue Zhu. Jiayu Song, Xixin Zhao +4
Offline-to-online reinforcement learning (O2O-RL) has emerged as a promising paradigm for safe and efficient robotic policy deployment but suffers from two fundamental challenges:…
UMDAM: A Unified Data Layout and DRAM Address Mapping for Heterogenous NPU-PIM
Hai Huang
Large Language Models (LLMs) are increasingly deployed on edge devices with Neural Processing Units (NPUs), yet the decode phase remains memory-intensive, limiting performance. Pro…
LLM-JEPA: Large Language Models Meet Joint Embedding Predictive Architectures
Hai Huang, Yann LeCun, Randall Balestriero
Large Language Model (LLM) pretraining, finetuning, and evaluation rely on input-space reconstruction and generative capabilities. Yet, it has been observed in vision that embeddin…
Synergistic Modality-and-Slice Memory Framework for Cross-Modal 3D Brain Tumor Segmentation
Yuxiang Luo, Qing Xu, Hai Huang +5
The 3D multi-modal brain tumor segmentation is critical to multi-modal healthcare, and it requires accurate identification of distinct internal anatomical subregions. While the rec…
Directed Information -covering: An Information-Theoretic Framework for Context Engineering
Hai Huang
We introduce \textbf{Directed Information -covering}, a simple but general framework for redundancy-aware context engineering. Directed information (DI), a causal analogue of mu…
LoRA Users Beware: A Few Spurious Tokens Can Manipulate Your Finetuned Model
Marcel Mateos Salles, Praney Goyal, Pradyut Sekhsaria +2
Large Language Models (LLMs) are commonly finetuned for a variety of use cases and domains. A common approach is to leverage Low-Rank Adaptation (LoRA) -- known to provide strong p…
Next Token Perception Score: Analytical Assessment of your LLM Perception Skills
Yu-Ang Cheng, Leyang Hu, Hai Huang +1
Autoregressive pretraining has become the de facto paradigm for learning general-purpose representations in large language models (LLMs). However, linear probe performance across d…
ALLoRA: Adaptive Learning Rate Mitigates LoRA Fatal Flaws
Hai Huang, Randall Balestriero
Low-Rank Adaptation (LoRA) is the bread and butter of Large Language Model (LLM) finetuning. LoRA learns an additive low-rank perturbation, , of a pretrained matrix parameter $…
CVPR 2023 Text Guided Video Editing Competition
Jay Zhangjie Wu, Xiuyu Li, Difei Gao +17
Humans watch more than a billion hours of video per day. Most of this video was edited manually, which is a tedious process. However, AI-enabled video-generation and video-editing…