LLM-Driven Composite Neural Architecture Search for Multi-Source RL State Encoding

• URL: http://arxiv.org/abs/2512.06982v2
• Date: Thu, 11 Dec 2025 18:52:44 GMT
• Title: LLM-Driven Composite Neural Architecture Search for Multi-Source RL State Encoding
• Authors: Yu Yu, Qian Xie, Nairen Cao, Li Jin,
• Abstract summary: Designing state encoders for reinforcement learning with multiple information sources remains underexplored and often requires manual design.<n>We formalize this challenge as a problem of composite neural architecture search (NAS), where multiple source-specific modules and a fusion module are jointly optimized.<n>We propose an LLM-driven NAS pipeline in which the LLM serves as a neural architecture design agent, leveraging language-model priors and intermediate-output signals.
• Score: 6.576358106930216
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Designing state encoders for reinforcement learning (RL) with multiple information sources -- such as sensor measurements, time-series signals, image observations, and textual instructions -- remains underexplored and often requires manual design. We formalize this challenge as a problem of composite neural architecture search (NAS), where multiple source-specific modules and a fusion module are jointly optimized. Existing NAS methods overlook useful side information from the intermediate outputs of these modules -- such as their representation quality -- limiting sample efficiency in multi-source RL settings. To address this, we propose an LLM-driven NAS pipeline in which the LLM serves as a neural architecture design agent, leveraging language-model priors and intermediate-output signals to guide sample-efficient search for high-performing composite state encoders. On a mixed-autonomy traffic control task, our approach discovers higher-performing architectures with fewer candidate evaluations than traditional NAS baselines and the LLM-based GENIUS framework.

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