arXiv:2605.30854v1 Announce Type: cross Abstract: Language models fine-tuned with reinforcement learning typically optimize for task reward, ignoring multi-agent strategic structure. Because these agents condition on natural language game-state descriptions and emit actions through free-form generation, strategic failure modes -- exploiting weaker opponents, coordinating on harmful equilibria, and externalizing costs are inseparable from the language interface itself. We propose Safe Equilibrium Policy Optimization (\sepo{}), a training objective that augments expected payoff with explicit pen
The proliferation of increasingly capable large language models necessitates robust methods for controlling their strategic interactions, especially as they become more autonomous.
This research addresses fundamental safety and alignment challenges in autonomous AI systems, which is critical for their responsible deployment and integration into complex real-world multi-agent environments.
The explicit focus on 'safe equilibrium' policies moves towards AI systems that are not only effective but also designed to prevent undesirable strategic outcomes, rather than simply optimizing for task reward.
- · AI Safety Researchers
- · Developers of multi-agent AI systems
- · Industries deploying autonomous AI
- · Malicious actors exploiting AI vulnerabilities
- · AI development prioritizing raw performance over safety
Increased development and deployment of AI agents in strategic, multi-agent environments.
Reduced incidence of AI-driven strategic failures or emergent undesirable behaviors in complex systems.
Enhanced public and regulatory confidence in the ethical development and deployment of advanced AI, potentially accelerating adoption.
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Read at arXiv cs.AI