Identified simplicity bias in RL policy networks that over‑prioritizes simple tasks.
Introduced a lightweight phase router that learns latent phase boundaries from RL objectives, without pre‑defined categories.
Designed PA‑MoE to allocate temporally consistent expert assignments, preserving phase‑specific expertise.
Provided experimental validation demonstrating the effectiveness of PA‑MoE over traditional token‑level routing.
Published the findings as a 16‑page arXiv preprint (arXiv:2602.17038).
📖 Full Retelling
Who: Researchers from Southeast University, Nanyang Technological University, and Kuaishou Technology – including Shengtian Yang, Yu Li, Shuo He, Yewen Li, Qingpeng Cai, Peng Jiang, and Lei Feng – introduced the study. What: They propose a Phase‑Aware Mixture of Experts (PA‑MoE) architecture to enhance reinforcement learning (RL) for large‑language‑model agents. Where: The work was submitted to arXiv (cs.AI) as paper arXiv:2602.17038. When: It was first posted on 19 Feb 2026. Why: Existing RL methods suffer from simplicity bias, where simple tasks dominate training and crowd out capacity for complex tasks; PA‑MoE aims to mitigate this by learning latent phase boundaries and assigning temporally consistent expert routes, thereby preserving specialized expertise for complex phases.
🏷️ Themes
Reinforcement Learning, Large‑Language‑Model Agents, Mixture of Experts Architecture, Phase‑Aware Routing, Model Capacity Allocation, Complex Task Handling, Simplicity Bias Mitigation
Entity Intersection Graph
No entity connections available yet for this article.
Deep Analysis
Why It Matters
The paper introduces a new architecture that addresses the simplicity bias in reinforcement learning for large language model agents, enabling more efficient learning of complex tasks. By using phase-aware routing, it preserves temporal consistency and improves expert specialization, which could lead to stronger performance in real-world agentic systems.
Context & Background
Reinforcement learning often suffers from simplicity bias where simple tasks dominate training.
Traditional MoE uses token-level routing, fragmenting phase patterns.
Phase-aware routing assigns temporally consistent tokens to experts.
The authors propose a lightweight phase router learned directly from the RL objective.
What Happens Next
Future work may integrate PA-MoE into large-scale LLM agents and evaluate performance on benchmark tasks. Researchers might also explore scaling the architecture and combining it with other efficiency techniques.
Frequently Asked Questions
What is simplicity bias?
Simplicity bias occurs when simple tasks consume most of the model's capacity, leading to suboptimal learning for complex tasks.
How does PA-MoE differ from traditional MoE?
PA-MoE uses a phase router that assigns temporally consistent tokens to experts, preserving phase-specific expertise instead of token-level fragmentation.
Can PA-MoE be applied to other domains?
Yes, the routing mechanism is general and could be adapted to any sequential decision-making problem where phase consistency matters.
What are the next steps for this research?
The authors plan to test PA-MoE on larger agents and benchmark datasets to demonstrate its scalability and effectiveness.
Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.17038 [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 Abstract: Reinforcement learning has equipped LLM agents with a strong ability to solve complex tasks. However, existing RL methods normally use a \emph 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 architecture in the policy network, as MoE allows different parameters 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. Comments: 16 pages Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.17038 [cs.AI] (or arXiv:2602.17038v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.17038 Focus to learn more arXiv-issued ...
