Risk-Aware Reinforcement Learning for Safe Strategic Reasoning in Large Language Model Agents
Keywords:
risk-aware reinforcement learning, large language model agents, safe strategic reasoning, coherent risk measures, socio-technical infrastructure, governance, fairnessAbstract
The rapid deployment of large language model (LLM) agents in high-stakes decision-making environments has introduced unprecedented challenges for ensuring safe and reliable strategic reasoning. Traditional reinforcement learning (RL) methods, while effective for optimizing long-term rewards, often neglect the systematic management of catastrophic risks that arise from distributional shift, adversarial manipulation, and unintended goal misgeneralization. This paper proposes a risk-aware reinforcement learning paradigm specifically designed for LLM agents engaged in strategic reasoning tasks. We argue that integrating coherent risk measures, such as conditional value-at-risk and entropic risk, into the RL objective enables agents to internalize downside exposure while preserving the exploratory benefits of standard RL. We examine the architectural trade-offs between model complexity, computational cost, and safety guarantees, and explore how risk-aware objectives interact with the autoregressive generation process of LLMs. The discussion extends to governance frameworks for deploying such agents in socio-technical infrastructures, addressing fairness, sustainability, and regulatory oversight. By synthesizing concepts from reinforcement learning theory, risk management, and large-scale system engineering, this paper provides a comprehensive analysis of how risk-aware RL can serve as a foundation for safe strategic reasoning in LLM agents. We conclude with forward-looking perspectives on policy implications and the need for interdisciplinary collaboration to ensure that intelligent agents operate within acceptable risk boundaries.
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