6 papers
Multi-Agent Deep Research: Training Multi-Agent Systems with M-GRPO
Haoyang Hong, Jiajun Yin, Yuan Wang +14
Multi-agent systems perform well on general reasoning tasks. However, the lack of training in specialized areas hinders their accuracy. Current training methods train a unified lar…
Self-Rewarding Rubric-Based Reinforcement Learning for Open-Ended Reasoning
Zhiling Ye, Yun Yue, Haowen Wang +11
Open-ended evaluation is essential for deploying large language models in real-world settings. In studying HealthBench, we observe that using the model itself as a grader and gener…
MedResearcher-R1: Expert-Level Medical Deep Researcher via A Knowledge-Informed Trajectory Synthesis Framework
Ailing Yu, Lan Yao, Jingnan Liu +13
Recent developments in Large Language Model (LLM)-based agents have shown impressive capabilities spanning multiple domains, exemplified by deep research systems that demonstrate s…
Learning to Align, Aligning to Learn: A Unified Approach for Self-Optimized Alignment
Haowen Wang, Yun Yue, Zhiling Ye +12
Alignment methodologies have emerged as a critical pathway for enhancing language model alignment capabilities. While SFT (supervised fine-tuning) accelerates convergence through d…
EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large Language Models
Jialiang Cheng, Ning Gao, Yun Yue +3
Distributed training methods are crucial for large language models (LLMs). However, existing distributed training methods often suffer from communication bottlenecks, stragglers, a…
AGD: an Auto-switchable Optimizer using Stepwise Gradient Difference for Preconditioning Matrix
Yun Yue, Zhiling Ye, Jiadi Jiang +2
Adaptive optimizers, such as Adam, have achieved remarkable success in deep learning. A key component of these optimizers is the so-called preconditioning matrix, providing enhance…