3 citations · 7 across the 13 of their papers we have counts for
6 papers · 1 filter
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.…