11 citations · 24 across the 33 of their papers we have counts for
14 papers · 1 filter
Pin Once, Swap Light: Subspace-Aligned Centroid-Residual Training for Efficient Ultra-LoRA Serving
Xiang Li, Pengcheng Wang, Huazheng Wang +1
Modern multi-tenant Low-Rank Adapters (LoRAs) serving systems concurrently host tens to hundreds of LoRA adapters. Though powerful, this introduces a critical system dilemma betwee…
From RLVR to RLSVR: Task Transformation Induces Self-Verifiable Rewards for Open-Ended LLM Self-Improvement
Qinsi Wang, Jing Shi, Huazheng Wang +8
Reinforcement Learning with Verifiable Rewards (RLVR) has driven recent progress in reasoning-oriented large language models (LLMs) by enabling large-scale optimization. However, i…
Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification
Haoyang Hong, Zichen Wang, Quanquan Gu +1
We study KL-regularized contextual bandits and episodic reinforcement learning (RL) under general function approximation with model misspecification. Existing guarantees rely on re…
Who&When Pro: Can LLMs Really Attribute Failures in AI Agents?
Jiale Liu, Huajun Xi, Shaokun Zhang +6
Automated failure attribution uses LLMs to identify where and why agentic systems fail. As agents become more capable, their failures become subtler, making automated attribution i…
EvoPool: Evolutionary Programmatic Annotation for Label-Efficient Specialized Supervision
Tianyi Xu, Yaolun Zhang, Xuan Ouyang +1
Large language models excel at general tasks but underperform smaller supervised models in specialized, high-stakes domains where training labels are costly. We address this regime…
When Does Multi-Agent RL Improve LLM Workflows? Workflow, Scale, and Policy-Sharing Tradeoffs
Yifan Zeng, Yiran Wu, Yaolun Zhang +4
Multi-agent LLM workflows route inference through specialized roles to lift end-task accuracy, but jointly training those roles with reinforcement learning is unstable in ways that…