Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning
Fuente:
arXiv
Saved in:
| Main Authors: | Ma, Guozheng, Li, Lu, Wang, Zilin, Shen, Li, Bacon, Pierre-Luc, Tao, Dacheng |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
What Makes Value Learning Efficient in Residual Reinforcement Learning?
by: Ma, Guozheng, et al.
Published: (2026)
by: Ma, Guozheng, et al.
Published: (2026)
Mastering Massive Multi-Task Reinforcement Learning via Mixture-of-Expert Decision Transformer
by: Kong, Yilun, et al.
Published: (2025)
by: Kong, Yilun, et al.
Published: (2025)
Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages
by: Ma, Guozheng, et al.
Published: (2023)
by: Ma, Guozheng, et al.
Published: (2023)
The Three Regimes of Offline-to-Online Reinforcement Learning
by: Li, Lu, et al.
Published: (2025)
by: Li, Lu, et al.
Published: (2025)
Plasticine: Accelerating Research in Plasticity-Motivated Deep Reinforcement Learning
by: Yuan, Mingqi, et al.
Published: (2025)
by: Yuan, Mingqi, et al.
Published: (2025)
Understanding Behavioral Metric Learning: A Large-Scale Study on Distracting Reinforcement Learning Environments
by: Luo, Ziyan, et al.
Published: (2025)
by: Luo, Ziyan, et al.
Published: (2025)
Rethinking the Role of Dynamic Sparse Training for Scalable Deep Reinforcement Learning
by: Ma, Guozheng, et al.
Published: (2025)
by: Ma, Guozheng, et al.
Published: (2025)
Neuron-level Balance between Stability and Plasticity in Deep Reinforcement Learning
by: Lan, Jiahua, et al.
Published: (2025)
by: Lan, Jiahua, et al.
Published: (2025)
Solving Continual Offline Reinforcement Learning with Decision Transformer
by: Huang, Kaixin, et al.
Published: (2024)
by: Huang, Kaixin, et al.
Published: (2024)
Layerwise LQR for Geometry-Aware Optimization of Deep Networks
by: Dufort-Labbé, Simon, et al.
Published: (2026)
by: Dufort-Labbé, Simon, et al.
Published: (2026)
On the Interplay Between Sparsity and Training in Deep Reinforcement Learning
by: Davelouis, Fatima, et al.
Published: (2025)
by: Davelouis, Fatima, et al.
Published: (2025)
Task-Aware Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning
by: Fan, Ziqing, et al.
Published: (2024)
by: Fan, Ziqing, et al.
Published: (2024)
Rotation-Preserving Supervised Fine-Tuning
by: Jin, Hangzhan, et al.
Published: (2026)
by: Jin, Hangzhan, et al.
Published: (2026)
Continual Diffuser (CoD): Mastering Continual Offline Reinforcement Learning with Experience Rehearsal
by: Hu, Jifeng, et al.
Published: (2024)
by: Hu, Jifeng, et al.
Published: (2024)
Beyond Token-level Supervision: Unlocking the Potential of Decoding-based Regression via Reinforcement Learning
by: Chen, Ming, et al.
Published: (2025)
by: Chen, Ming, et al.
Published: (2025)
Analytic Energy-Guided Policy Optimization for Offline Reinforcement Learning
by: Hu, Jifeng, et al.
Published: (2025)
by: Hu, Jifeng, et al.
Published: (2025)
Do Transformer World Models Give Better Policy Gradients?
by: Ma, Michel, et al.
Published: (2024)
by: Ma, Michel, et al.
Published: (2024)
Efficient Differentiable Causal Discovery via Reliable Super-Structure Learning
by: Ma, Pingchuan, et al.
Published: (2026)
by: Ma, Pingchuan, et al.
Published: (2026)
Unlocking Tuning-Free Few-Shot Adaptability in Visual Foundation Models by Recycling Pre-Tuned LoRAs
by: Hu, Zixuan, et al.
Published: (2024)
by: Hu, Zixuan, et al.
Published: (2024)
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning
by: Hu, Zixuan, et al.
Published: (2023)
by: Hu, Zixuan, et al.
Published: (2023)
Adaptive Defense against Harmful Fine-Tuning for Large Language Models via Bayesian Data Scheduler
by: Hu, Zixuan, et al.
Published: (2025)
by: Hu, Zixuan, et al.
Published: (2025)
Sparsity-Driven Plasticity in Multi-Task Reinforcement Learning
by: Todorov, Aleksandar, et al.
Published: (2025)
by: Todorov, Aleksandar, et al.
Published: (2025)
HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving
by: Huang, Zilin, et al.
Published: (2024)
by: Huang, Zilin, et al.
Published: (2024)
Task-Distributionally Robust Data-Free Meta-Learning
by: Hu, Zixuan, et al.
Published: (2023)
by: Hu, Zixuan, et al.
Published: (2023)
Reward Redistribution for CVaR MDPs using a Bellman Operator on L-infinity
by: Muni, Aneri, et al.
Published: (2026)
by: Muni, Aneri, et al.
Published: (2026)
Scaling Attention via Feature Sparsity
by: Xie, Yan, et al.
Published: (2026)
by: Xie, Yan, et al.
Published: (2026)
Stackelberg Coupling of Online Representation Learning and Reinforcement Learning
by: Martinez, Fernando, et al.
Published: (2025)
by: Martinez, Fernando, et al.
Published: (2025)
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)
Sparsity-based Safety Conservatism for Constrained Offline Reinforcement Learning
by: Cho, Minjae, et al.
Published: (2024)
by: Cho, Minjae, et al.
Published: (2024)
Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization
by: Pang, Jing-Cheng, et al.
Published: (2021)
by: Pang, Jing-Cheng, et al.
Published: (2021)
How Should We Meta-Learn Reinforcement Learning Algorithms?
by: Goldie, Alexander David, et al.
Published: (2025)
by: Goldie, Alexander David, et al.
Published: (2025)
Dynamic Sparsity: Challenging Common Sparsity Assumptions for Learning World Models in Robotic Reinforcement Learning Benchmarks
by: Pandaram, Muthukumar, et al.
Published: (2025)
by: Pandaram, Muthukumar, et al.
Published: (2025)
A priori Estimates for Deep Residual Network in Continuous-time Reinforcement Learning
by: Yin, Shuyu, et al.
Published: (2024)
by: Yin, Shuyu, et al.
Published: (2024)
Bridging State and History Representations: Understanding Self-Predictive RL
by: Ni, Tianwei, et al.
Published: (2024)
by: Ni, Tianwei, et al.
Published: (2024)
ELAS: Efficient Pre-Training of Low-Rank Large Language Models via 2:4 Activation Sparsity
by: Li, Jiaxi, et al.
Published: (2026)
by: Li, Jiaxi, et al.
Published: (2026)
Continual Learning on Graphs: Challenges, Solutions, and Opportunities
by: Zhang, Xikun, et al.
Published: (2024)
by: Zhang, Xikun, et al.
Published: (2024)
Mildly Conservative Q-Learning for Offline Reinforcement Learning
by: Lyu, Jiafei, et al.
Published: (2022)
by: Lyu, Jiafei, et al.
Published: (2022)
Continual Task Learning through Adaptive Policy Self-Composition
by: Hu, Shengchao, et al.
Published: (2024)
by: Hu, Shengchao, et al.
Published: (2024)
Revisiting Entropy Regularization: Adaptive Coefficient Unlocks Its Potential for LLM Reinforcement Learning
by: Zhang, Xiaoyun, et al.
Published: (2025)
by: Zhang, Xiaoyun, et al.
Published: (2025)
Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey
by: Zong, Zefang, et al.
Published: (2021)
by: Zong, Zefang, et al.
Published: (2021)
Similar Items
-
What Makes Value Learning Efficient in Residual Reinforcement Learning?
by: Ma, Guozheng, et al.
Published: (2026) -
Mastering Massive Multi-Task Reinforcement Learning via Mixture-of-Expert Decision Transformer
by: Kong, Yilun, et al.
Published: (2025) -
Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages
by: Ma, Guozheng, et al.
Published: (2023) -
The Three Regimes of Offline-to-Online Reinforcement Learning
by: Li, Lu, et al.
Published: (2025) -
Plasticine: Accelerating Research in Plasticity-Motivated Deep Reinforcement Learning
by: Yuan, Mingqi, et al.
Published: (2025)