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cs.LG2024
Learning World Models for Unconstrained Goal Navigation
Yuanlin Duan, Wensen Mao, He Zhu
Learning world models offers a promising avenue for goal-conditioned reinforcement learning with sparse rewards. By allowing agents to plan actions or exploratory goals without dir…
cs.LG2024
Exploring the Edges of Latent State Clusters for Goal-Conditioned Reinforcement Learning
Yuanlin Duan, Guofeng Cui, He Zhu
Exploring unknown environments efficiently is a fundamental challenge in unsupervised goal-conditioned reinforcement learning. While selecting exploratory goals at the frontier of…
cs.LG2024
MoE-I: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition
Cheng Yang, Yang Sui, Jinqi Xiao +7
The emergence of Mixture of Experts (MoE) LLMs has significantly advanced the development of language models. Compared to traditional LLMs, MoE LLMs outperform traditional LLMs by…