arXiv:2605.28918v1 Announce Type: new Abstract: For sparse, structured reinforcement-learning tasks with semantic reward-function interfaces, LLM-generated reward shaping is better framed as debugging than one-shot generation. We study PPO-trained agents using MiniGrid as core evaluation and MuJoCo as boundary stress test. Our audit finds two dominant one-shot failure modes -- reward flooding and semantic/API misunderstanding -- plus a rarer weak-shaping case. We propose diagnostic-driven iterative refinement, where training diagnostics and a failure-mode taxonomy guide targeted reward-functio
The rapid advancement and deployment of LLMs into complex automation tasks necessitate robust methods for ensuring reliable agent behavior, making LLM reward design a critical area of focus.
This research provides a diagnostic framework to overcome key failure modes in LLM-generated reward functions for sparse, structured reinforcement learning, directly impacting the reliability and scalability of AI agents.
The shift from one-shot reward generation to an iterative, diagnostic-driven refinement approach enhances the robustness and explainability of AI agent development, addressing a major bottleneck in agent performance.
- · AI agents developers
- · Reinforcement learning researchers
- · Companies building autonomous systems
- · One-shot reward generation approaches
- · Systems highly reliant on unrefined LLM-based reward functions
More reliable and robust AI agents can be developed for complex, real-world tasks.
The improved performance of AI agents could accelerate their adoption across various industries, leading to increased automation.
This could contribute to the collapsing of white-collar workflows and SaaS layers as autonomous systems become more capable and trustworthy.
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Read at arXiv cs.LG