1k citations · 1.8k across the 17 of their papers we have counts for
18 papers
PILE: Pairwise Iterative Logits Ensemble for Multi-Teacher Labeled Distillation
Lianshang Cai, Linhao Zhang, Dehong Ma +6
Pre-trained language models have become a crucial part of ranking systems and achieved very impressive effects recently. To maintain high performance while keeping efficient comput…
Understanding Curriculum Learning in Policy Optimization for Online Combinatorial Optimization
Runlong Zhou, Zelin He, Yuandong Tian +2
Over the recent years, reinforcement learning (RL) starts to show promising results in tackling combinatorial optimization (CO) problems, in particular when coupled with curriculum…
Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination
Rui Zhao, Jinming Song, Yufeng Yuan +5
We study the problem of training a Reinforcement Learning (RL) agent that is collaborative with humans without using any human data. Although such agents can be obtained through se…
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning
Qian Long, Zihan Zhou, Abhibav Gupta +3
In multi-agent games, the complexity of the environment can grow exponentially as the number of agents increases, so it is particularly challenging to learn good policies when the…
Multi-Task Reinforcement Learning with Soft Modularization
Ruihan Yang, Huazhe Xu, Yi Wu +1
Multi-task learning is a very challenging problem in reinforcement learning. While training multiple tasks jointly allow the policies to share parameters across different tasks, th…
Influence-Based Multi-Agent Exploration
Tonghan Wang, Jianhao Wang, Yi Wu +1
Intrinsically motivated reinforcement learning aims to address the exploration challenge for sparse-reward tasks. However, the study of exploration methods in transition-dependent…