5 papers
IAPO: Input Attribution-Aware Policy Optimization for Tool Use in Small Multimodal Agents
Yifan Yang, Zhen Zhang, Jiayi Tian +2
This paper investigates reinforcement learning (RL) methods for improving tool-calling capabilities in multimodal small language model (SLM) agents. While existing works have explo…
ZOAF: Towards Efficient Zeroth-Order Optimization for Analog/RF Circuit Design
Liyan Tan, Yequan Zhao, Jinming Lu +3
Circuit optimization is an indispensable step in analog/RF IC design. Classical fast gradient-based optimization methods are typically infeasible due to lack of access to simulator…
GRZO: Group-Relative Zeroth-Order Optimization for Large Language Model Fine-Tuning
Liyan Tan, Yequan Zhao, Yifan Yang +3
Zeroth-order (ZO) optimization is a memory-efficient alternative to backpropagation for fine-tuning large language models, but its deployment is limited by the high variance of gra…
FuRA: Full-Rank Parameter-Efficient Fine-Tuning with Spectral Preconditioning
Yequan Zhao, Ruijie Zhang, Liyan Tan +3
Both full fine-tuning (Full FT) and parameter-efficient fine-tuning methods such as LoRA introduce weight updates without accounting for the spectral structure established during p…
MUON+: Towards More Effective Muon via One Additional Normalization Step for LLM Pre-training
Ruijie Zhang, Yequan Zhao, Ziyue Liu +4
Muon has recently emerged as a strong optimizer for large language model pre-training, orthogonalizing the momentum matrix via Newton--Schulz polar iterations. A natural intuition…