1 citations · 3 across the 13 of their papers we have counts for
13 papers
Reasoning-Preserving Fine-Tuning of Post-RL LLMs with Null-Basis LoRA
Wenzhi Fang, Nicholas Tzou, Lazar Valkov +1
Reinforcement learning (RL)-based post-training has become an effective approach for eliciting reasoning capabilities in large language models (LLMs). However, adapting post-RL mod…
Iterative Critique-and-Routing Controller for Multi-Agent Systems with Heterogeneous LLMs
Wenzhi Fang, Liangqi Yuan, Guangchen Lan +2
Multi-agent large language model (LLM) systems often rely on a controller to coordinate a pool of heterogeneous models, yet existing controllers are typically limited to one-shot r…
PAAC: Privacy-Aware Agentic Device-Cloud Collaboration
Liangqi Yuan, Wenzhi Fang, Shiqiang Wang +1
Large language model (LLM) agents face a structural tension: cloud agents provide strong reasoning but expose user data, while on-device agents preserve privacy at the cost of over…
Large Language Models over Networks: Collaborative Intelligence under Resource Constraints
Liangqi Yuan, Wenzhi Fang, Shiqiang Wang +2
Large language models (LLMs) are transforming society, powering applications from smartphone assistants to autonomous driving. Yet cloud-based LLM services alone cannot serve a gro…
Self-Play Enhancement via Advantage-Weighted Refinement in Online Federated LLM Fine-Tuning with Real-Time Feedback
Seohyun Lee, Wenzhi Fang, Dong-Jun Han +2
Recent works have advanced feedback-based learning systems, whereby a foundation model is able to intake incoming feedback (e.g., a user) to self-improve, creating a self-loop syst…
Joint Continual Learning of Local Language Models and Cloud Offloading Decisions with Budget Constraints
Evan Chen, Wenzhi Fang, Shiqiang Wang +1
Locally deployed Small Language Models (SLMs) must continually support diverse tasks under strict memory and computation constraints, making selective reliance on cloud Large Langu…