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[2602.17038] Phase-Aware Mixture of Experts for Agentic Reinforcement Learning
Summary
The paper presents a novel Phase-Aware Mixture of Experts (PA-MoE) architecture for reinforcement learning, addressing the limitations of traditional methods that suffer from simplicity bias by allowing experts to specialize in phase-consistent tasks.
Why It Matters
This research is significant as it enhances the efficiency of reinforcement learning models by improving task specialization. By mitigating simplicity bias, PA-MoE can lead to better performance in complex environments, which is crucial for advancing AI capabilities in real-world applications.
Key Takeaways
- PA-MoE architecture allows for specialization of experts in reinforcement learning tasks.
- Mitigates simplicity bias by preventing simple tasks from dominating model parameters.
- Introduces a lightweight phase router that learns phase boundaries from the RL objective.
Computer Science > Artificial Intelligence arXiv:2602.17038 (cs) [Submitted on 19 Feb 2026] Title:Phase-Aware Mixture of Experts for Agentic Reinforcement Learning Authors:Shengtian Yang (1 and 3), Yu Li (1), Shuo He (2), Yewen Li (3), Qingpeng Cai (3), Peng Jiang (3), Lei Feng (1) ((1) Southeast University, (2) Nanyang Technological University, (3) Kuaishou Technology) View a PDF of the paper titled Phase-Aware Mixture of Experts for Agentic Reinforcement Learning, by Shengtian Yang (1 and 3) and 8 other authors View PDF HTML (experimental) Abstract:Reinforcement learning (RL) has equipped LLM agents with a strong ability to solve complex tasks. However, existing RL methods normally use a \emph{single} policy network, causing \emph{simplicity bias} where simple tasks occupy most parameters and dominate gradient updates, leaving insufficient capacity for complex tasks. A plausible remedy could be employing the Mixture-of-Experts (MoE) architecture in the policy network, as MoE allows different parameters (experts) to specialize in different tasks, preventing simple tasks from dominating all parameters. However, a key limitation of traditional MoE is its token-level routing, where the router assigns each token to specialized experts, which fragments phase-consistent patterns into scattered expert assignments and thus undermines expert specialization. In this paper, we propose \textbf{Phase-Aware Mixture of Experts (PA-MoE)}. It first features a lightweight \emph{phase route...
