Phase-Aware Mixture of Experts for Agentic Reinforcement Learning

• URL: http://arxiv.org/abs/2602.17038v1
• Date: Thu, 19 Feb 2026 03:18:30 GMT
• Title: Phase-Aware Mixture of Experts for Agentic Reinforcement Learning
• Authors: Shengtian Yang, Yu Li, Shuo He, Yewen Li, Qingpeng Cai, Peng Jiang, Lei Feng,
• Abstract summary: A plausible remedy could be employing the Mixture-of-Experts (MoE) architecture in the policy network.<n>MoE allows different parameters (experts) to specialize in different tasks, preventing simple tasks from dominating all parameters.<n>We propose textbfPhase-Aware Mixture of Experts (PA-MoE).<n>It first features a lightweight emphphase router that learns latent phase boundaries directly from the RL objective without pre-defining phase categories.<n>Then, the phase router allocates temporally consistent assignments to the same expert, allowing experts to preserve phase-specific expertise
• Score: 23.18318273534301
• License: http://creativecommons.org/licenses/by/4.0/
• 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 router} that learns latent phase boundaries directly from the RL objective without pre-defining phase categories. Then, the phase router allocates temporally consistent assignments to the same expert, allowing experts to preserve phase-specific expertise. Experimental results demonstrate the effectiveness of our proposed PA-MoE.

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