Mixture of Experts in a Mixture of RL settings
Fuente:
arXiv
Saved in:
| Main Authors: | Willi, Timon, Obando-Ceron, Johan, Foerster, Jakob, Dziugaite, Karolina, Castro, Pablo Samuel |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Mixtures of Experts Unlock Parameter Scaling for Deep RL
by: Obando-Ceron, Johan, et al.
Published: (2024)
by: Obando-Ceron, Johan, et al.
Published: (2024)
In value-based deep reinforcement learning, a pruned network is a good network
by: Obando-Ceron, Johan, et al.
Published: (2024)
by: Obando-Ceron, Johan, et al.
Published: (2024)
Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL
by: Sokar, Ghada, et al.
Published: (2024)
by: Sokar, Ghada, et al.
Published: (2024)
The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks
by: Mayor, Walter, et al.
Published: (2025)
by: Mayor, Walter, et al.
Published: (2025)
No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery
by: Rutherford, Alexander, et al.
Published: (2024)
by: Rutherford, Alexander, et al.
Published: (2024)
Less is More: Undertraining Experts Improves Model Upcycling
by: Horoi, Stefan, et al.
Published: (2025)
by: Horoi, Stefan, et al.
Published: (2025)
On the consistency of hyper-parameter selection in value-based deep reinforcement learning
by: Obando-Ceron, Johan, et al.
Published: (2024)
by: Obando-Ceron, Johan, et al.
Published: (2024)
Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn
by: Tang, Hongyao, et al.
Published: (2025)
by: Tang, Hongyao, et al.
Published: (2025)
Stable Deep Reinforcement Learning via Isotropic Gaussian Representations
by: Pasand, Ali Saheb, et al.
Published: (2026)
by: Pasand, Ali Saheb, et al.
Published: (2026)
Simplicial Embeddings Improve Sample Efficiency in Actor-Critic Agents
by: Obando-Ceron, Johan, et al.
Published: (2025)
by: Obando-Ceron, Johan, et al.
Published: (2025)
Mixture of Latent Experts Using Tensor Products
by: Su, Zhan, et al.
Published: (2024)
by: Su, Zhan, et al.
Published: (2024)
Mixture of Raytraced Experts
by: Perin, Andrea, et al.
Published: (2025)
by: Perin, Andrea, et al.
Published: (2025)
MC#: Mixture Compressor for Mixture-of-Experts Large Models
by: Huang, Wei, et al.
Published: (2025)
by: Huang, Wei, et al.
Published: (2025)
Analysing the Sample Complexity of Opponent Shaping
by: Fung, Kitty, et al.
Published: (2024)
by: Fung, Kitty, et al.
Published: (2024)
A Comedy of Estimators: On KL Regularization in RL Training of LLMs
by: Shah, Vedant, et al.
Published: (2025)
by: Shah, Vedant, et al.
Published: (2025)
Mixture of Diverse Size Experts
by: Sun, Manxi, et al.
Published: (2024)
by: Sun, Manxi, et al.
Published: (2024)
Mixture of A Million Experts
by: He, Xu Owen
Published: (2024)
by: He, Xu Owen
Published: (2024)
Sparsity and Superposition in Mixture of Experts
by: Chaudhari, Marmik, et al.
Published: (2025)
by: Chaudhari, Marmik, et al.
Published: (2025)
Mixture of Concept Bottleneck Experts
by: De Santis, Francesco, et al.
Published: (2026)
by: De Santis, Francesco, et al.
Published: (2026)
Speculating Experts Accelerates Inference for Mixture-of-Experts
by: Madan, Vivan, et al.
Published: (2026)
by: Madan, Vivan, et al.
Published: (2026)
A Mechanistic Analysis of Looped Reasoning Language Models
by: Blayney, Hugh, et al.
Published: (2026)
by: Blayney, Hugh, et al.
Published: (2026)
Efficiently Editing Mixture-of-Experts Models with Compressed Experts
by: He, Yifei, et al.
Published: (2025)
by: He, Yifei, et al.
Published: (2025)
Graph Knowledge Distillation to Mixture of Experts
by: Rumiantsev, Pavel, et al.
Published: (2024)
by: Rumiantsev, Pavel, et al.
Published: (2024)
Theory on Mixture-of-Experts in Continual Learning
by: Li, Hongbo, et al.
Published: (2024)
by: Li, Hongbo, et al.
Published: (2024)
Mixture of Weak & Strong Experts on Graphs
by: Zeng, Hanqing, et al.
Published: (2023)
by: Zeng, Hanqing, et al.
Published: (2023)
Mixture of Experts in Large Language Models
by: Zhang, Danyang, et al.
Published: (2025)
by: Zhang, Danyang, et al.
Published: (2025)
JaxUED: A simple and useable UED library in Jax
by: Coward, Samuel, et al.
Published: (2024)
by: Coward, Samuel, et al.
Published: (2024)
MixtureKit: A General Framework for Composing, Training, and Visualizing Mixture-of-Experts Models
by: Chamma, Ahmad, et al.
Published: (2025)
by: Chamma, Ahmad, et al.
Published: (2025)
Accelerating Mixture-of-Expert Inference with Adaptive Expert Split Mechanism
by: Yan, Jiaming, et al.
Published: (2025)
by: Yan, Jiaming, et al.
Published: (2025)
Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts
by: Dwivedi, Chaitanya, et al.
Published: (2026)
by: Dwivedi, Chaitanya, et al.
Published: (2026)
The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind
by: Lupu, Andrei, et al.
Published: (2025)
by: Lupu, Andrei, et al.
Published: (2025)
Mixture-of-Experts Meets In-Context Reinforcement Learning
by: Wu, Wenhao, et al.
Published: (2025)
by: Wu, Wenhao, et al.
Published: (2025)
Wavelet Mixture of Experts for Time Series Forecasting
by: Zhou, Zheng, et al.
Published: (2025)
by: Zhou, Zheng, et al.
Published: (2025)
AnyExperts: On-Demand Expert Allocation for Multimodal Language Models with Mixture of Expert
by: Gao, Yuting, et al.
Published: (2025)
by: Gao, Yuting, et al.
Published: (2025)
Dynamic Expert Quantization for Scalable Mixture-of-Experts Inference
by: Chu, Kexin, et al.
Published: (2025)
by: Chu, Kexin, et al.
Published: (2025)
Multi-Head Mixture-of-Experts
by: Wu, Xun, et al.
Published: (2024)
by: Wu, Xun, et al.
Published: (2024)
Routing-Free Mixture-of-Experts
by: Liu, Yilun, et al.
Published: (2026)
by: Liu, Yilun, et al.
Published: (2026)
Multilingual Routing in Mixture-of-Experts
by: Bandarkar, Lucas, et al.
Published: (2025)
by: Bandarkar, Lucas, et al.
Published: (2025)
Understanding Expert Structures on Minimax Parameter Estimation in Contaminated Mixture of Experts
by: Yan, Fanqi, et al.
Published: (2024)
by: Yan, Fanqi, et al.
Published: (2024)
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
by: Jin, Peng, et al.
Published: (2024)
by: Jin, Peng, et al.
Published: (2024)
Similar Items
-
Mixtures of Experts Unlock Parameter Scaling for Deep RL
by: Obando-Ceron, Johan, et al.
Published: (2024) -
In value-based deep reinforcement learning, a pruned network is a good network
by: Obando-Ceron, Johan, et al.
Published: (2024) -
Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL
by: Sokar, Ghada, et al.
Published: (2024) -
The Impact of On-Policy Parallelized Data Collection on Deep Reinforcement Learning Networks
by: Mayor, Walter, et al.
Published: (2025) -
No Regrets: Investigating and Improving Regret Approximations for Curriculum Discovery
by: Rutherford, Alexander, et al.
Published: (2024)