arXiv:2605.26657v1 Announce Type: new Abstract: Long-horizon decision problems with cumulative damage couple locally attractive actions to globally adverse outcomes. We identify two orthogonal failure modes for policy-gradient methods on this class and propose a decomposition that separates them: \emph{completion} (reaching the terminal horizon rather than exiting via an implicit terminal constraint) and \emph{optimality} (matching the dynamic-programming reference given completion). Under PPO with a linear soft penalty, granting horizon access alone reduces the completion rate: the penalty's
This paper addresses a fundamental challenge in AI agent development, which is increasingly focused on long-horizon tasks and real-world deployment where cumulative damage is a critical consideration. The increasing complexity of AI applications necessitates more robust and reliable control mechanisms.
Improving policy-gradient methods to handle long-horizon problems with cumulative damage is crucial for developing safe and effective AI agents in critical applications. It directly impacts the reliability and trustworthiness of autonomous systems operating over extended periods.
Current policy-gradient methods are shown to have specific failure modes in cumulative-damage problems, leading to suboptimal or dangerous outcomes; this research identifies and proposes a decomposition to address these, potentially leading to more stable and robust AI agent training.
- · AI researchers
- · Developers of autonomous systems
- · Sectors using AI for long-term operations
- · AI systems prone to catastrophic failure
Improved understanding and mitigation of failure modes in AI agents operating in complex, dynamic environments.
Faster and safer deployment of AI agents in high-stakes fields like defense, advanced manufacturing, and critical infrastructure.
Enhanced public trust and regulatory acceptance of autonomous AI systems as they demonstrate greater reliability and predictability over time.
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Read at arXiv cs.AI