7 citations · 15 across the 12 of their papers we have counts for
3 papers · 1 filter
Knapsack RL: Unlocking Exploration of LLMs via Optimizing Budget Allocation
Ziniu Li, Congliang Chen, Tianyun Yang +5
Large Language Models (LLMs) can self-improve through reinforcement learning, where they generate trajectories to explore and discover better solutions. However, this exploration p…
Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives
Yajiao Liu, Congliang Chen, Junchi Yang +1
Training large language models with data collected from various domains can improve their performance on downstream tasks. However, given a fixed training budget, the sampling prop…
Towards Quantifying the Hessian Structure of Neural Networks
Zhaorui Dong, Yushun Zhang, Jianfeng Yao +1
Empirical studies reported that the Hessian matrix of neural networks (NNs) exhibits a near-block-diagonal structure, yet its theoretical foundation remains unclear. In this work,…