4 citations · 4 across the 3 of their papers we have counts for
4 papers
Scalable In-Context Reinforcement Learning with Recurrent Algorithm Distillation
Yuanqing Ma, Zhenrui Zheng, Chenjun Xiao
Algorithm Distillation (AD) has demonstrated the remarkable ability of Transformers to perform in-context reinforcement learning without explicit weight updates. However, capturing…
Kimi K3: Open Frontier Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +398
We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…
Scaling DRL for Decision Making: A Survey on Data, Network, and Training Budget Strategies
Yi Ma, Hongyao Tang, Chenjun Xiao +4
In recent years, the expansion of neural network models and training data has driven remarkable progress in deep learning, particularly in computer vision and natural language proc…
Rethinking Decision Transformer via Hierarchical Reinforcement Learning
Yi Ma, Chenjun Xiao, Hebin Liang +1
Decision Transformer (DT) is an innovative algorithm leveraging recent advances of the transformer architecture in reinforcement learning (RL). However, a notable limitation of DT…