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

• URL: http://arxiv.org/abs/2608.18008v1
• Date: Tue, 18 Aug 2026 16:55:46 GMT
• Title: Policy-Invariant Reward Shaping from LLM Feedback: A Framework for Hybrid RL Agents
• Abstract summary: We formalize the hybrid LLM-planner and RL-controller architecture as a Goal-Augmented Markov Decision Process.<n>We 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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