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arXiv · 2608.18008

Policy-Invariant Reward Shaping from LLM Feedback: A Framework for Hybrid RL Agents

Christophe D. Hounwanou·John Emeka Eze·Yaé U. Gaba
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Abstract

Combining large language models with reinforcement learning is increasingly explored, yet the theoretical status of LLM-derived reward signals is often left implicit. We formalize the hybrid LLM-planner and RL-controller architecture as a Goal-Augmented Markov Decision Process and show that when the LLM per-state progress score is used as a bounded potential function, the resulting shaping term preserves the optimal policy set even when the LLM scores are inaccurate. This guarantee is stronger than what general LLM-as-reward approaches provide. We verify the result numerically on a small MDP under four potential configurations, including an adversarial one scaled to twenty times the base reward magnitude.

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Christophe D. Hounwanou, John Emeka Eze, Yaé U. Gaba. 2026-08-18. Policy-Invariant Reward Shaping from LLM Feedback: A Framework for Hybrid RL Agents. https://arxiv.org/abs/2608.18008

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