3 citations · 7 across the 13 of their papers we have counts for
15 papers
Beyond Policy Rollouts: Embedding-Space Directional Exploration with Anchored Noise for LLM RLVR
Daijiao Liu, Huiyi Wang, Lina Yao +1
Reinforcement learning with verifiable rewards (RLVR) typically uses small rollout groups per prompt, where repeated sampling from the same policy can yield highly similar trajecto…
ASCENT: Online Test-Time Training of Long-Horizon Agents via Self-Distillation of Verified Experience
Haodong Lu, Dong Gong
A large language model (LLM) agent solves long-horizon tasks through many reasoning-action turns, with one verification signal at termination. Deployed agents face streams of relat…
One Rate Is Not Enough: Adaptive Anisotropic Learning Rates for LoRA Fine-Tuning
Huiyi Wang, Daijiao Liu, Lina Yao +1
Low-rank adaptation (LoRA) has become the standard for parameter-efficient fine-tuning of large language models. Most LoRA variants follow a uniform-LR convention, applying a singl…
Impute On-Demand: Adaptive Correlated Time Series Imputation for Changing Environments
Zhichen Lai, Huan Li, Dalin Zhang +3
Internet of Things (IoT) applications generate vast amounts of Correlated Time Series (CTS) data that often contain missing values and require imputation. Existing methods emphasiz…
MILES: Modular Instruction Memory with Learnable Selection for Self-Improving LLM Reasoning
Ruilin Tong, Dong Gong
Large language models (LLMs) increasingly improve their reasoning at test time via additional computation, yet most existing works treat each problem in isolation. When problems ar…
On Token's Dilemma: Dynamic MoE with Drift-Aware Token Assignment for Continual Learning of Large Vision Language Models
Chongyang Zhao, Mingsong Li, Haodong Lu +1
Multimodal Continual Instruction Tuning aims to continually enhance Large Vision Language Models (LVLMs) by learning from new data without forgetting previously acquired knowledge.…
Model Inversion with Layer-Specific Modeling and Alignment for Data-Free Continual Learning
Ruilin Tong, Haodong Lu, Yuhang Liu +1
Continual learning (CL) aims to incrementally train a model on a sequence of tasks while retaining performance on prior ones. However, storing and replaying data is often infeasibl…
Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts
Haodong Lu, Chongyang Zhao, Minhui Xue +3
Continual learning (CL) with large pre-trained models aims to incrementally acquire knowledge without catastrophic forgetting. Existing LoRA-based Mixture-of-Experts (MoE) methods…
Take Only What You Need: Rank Minimization as an Implicit Forgetting Regularizer in Continual Learning
Haodong Lu, Chongyang Zhao, Minhui Xue +3
The central tension in continual learning (CL) is the trade-off between plasticity (acquiring new knowledge) and stability (retaining prior knowledge). We study how a pre-trained b…
Learning Mamba as a Continual Learner: Meta-Learning Selective State Space Models for Continual Learning
Chongyang Zhao, Dong Gong
Continual learning (CL) learns from a non-stationary data stream without storing or re-training on all seen samples. Meta-continual learning (MCL) casts CL as sequence prediction a…
Mining Your Own Secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models
Saurav Jha, Shiqi Yang, Masato Ishii +7
Personalized text-to-image diffusion models have grown popular for their ability to efficiently acquire a new concept from user-defined text descriptions and a few images. However,…
CLAP4CLIP: Continual Learning with Probabilistic Finetuning for Vision-Language Models
Saurav Jha, Dong Gong, Lina Yao
Continual learning (CL) aims to help deep neural networks learn new knowledge while retaining what has been learned. Owing to their powerful generalizability, pre-trained vision-la…
Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning
Huiyi Wang, Haodong Lu, Lina Yao +1
Continual learning (CL) aims to continually accumulate knowledge from a non-stationary data stream without catastrophic forgetting of learned knowledge, requiring a balance between…
Learning with Mixture of Prototypes for Out-of-Distribution Detection
Haodong Lu, Dong Gong, Shuo Wang +3
Out-of-distribution (OOD) detection aims to detect testing samples far away from the in-distribution (ID) training data, which is crucial for the safe deployment of machine learnin…
NPCL: Neural Processes for Uncertainty-Aware Continual Learning
Saurav Jha, Dong Gong, He Zhao +1
Continual learning (CL) aims to train deep neural networks efficiently on streaming data while limiting the forgetting caused by new tasks. However, learning transferable knowledge…