arXiv:2606.24669v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for planning and sequential decision-making, but prior work often relies on using them as direct controllers, which requires precise action generation and can be unreliable in practice. This paper proposes Latent Action Guidance for Online Reinforcement Learning (LaGO), a framework that uses a pretrained LLM as a latent action prior to softly guide online policy optimization, rather than treating the LLM as an explicit planner or controller. Experiments on both a discrete-control benchmark,
The increasing capabilities and scale of large language models are pushing researchers to find more robust and reliable ways to integrate them into complex decision-making systems like online reinforcement learning.
This development offers a practical approach to leveraging LLM intelligence in autonomous systems without direct control, mitigating risks of brittleness and paving the way for more sophisticated AI agents.
The method of using LLMs as latent action priors rather than explicit controllers allows for more stable and adaptable policy optimization in online reinforcement learning environments.
- · AI agents developers
- · Reinforcement learning researchers
- · Robotics sector
- · Autonomous systems integrators
- · Developers relying solely on direct LLM control
- · Systems with high fragility to LLM 'hallucinations'
Online reinforcement learning applications will become more robust and capable with the integration of LLM-guided latent action priors.
This improved reliability could accelerate the deployment of AI agents in real-world, dynamic environments across various industries.
More capable and trustworthy autonomous agents could significantly transform sectors such as complex logistics, advanced manufacturing, and strategic simulations.
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Read at arXiv cs.AI