Moral Alignment for LLM Agents

2024-10-02

arXiv.org

AI Summary

As large language model (LLM) agents gain enhanced capabilities, ensuring value alignment—particularly moral alignment—has become increasingly critical yet challenging. Method: This paper proposes an intrinsic reward modeling approach grounded in explicit moral principles, enabling moral fine-tuning via reinforcement learning without human preference data. It formalizes Kantian deontology and utilitarianism into computable moral reward functions that actively penalize selfish behavior. The agent is fine-tuned in the Iterated Prisoner’s Dilemma (IPD) and evaluated across diverse matrix games to assess cross-environment generalization of moral strategies. Contribution/Results: Experiments demonstrate stable emergence of cooperative policies, significant improvement in moral consistency, and robust transfer of ethical behavior across game-theoretic environments. The approach establishes a transparent, interpretable, and low-data-dependency paradigm for value alignment—offering a principled alternative to preference-based alignment methods.

Technology Category

• Philosophy and Ethics of AI: Morality & Value-based AI
• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Mechanism Design

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Decision-making agents based on pre-trained Large Language Models (LLMs) are increasingly being deployed across various domains of human activity. While their applications are currently rather specialized, several research efforts are under way to develop more generalist agents. As LLM-based systems become more agentic, their influence on human activity will grow and the transparency of this will decrease. Consequently, developing effective methods for aligning them to human values is vital. The prevailing practice in alignment often relies on human preference data (e.g., in RLHF or DPO), in which values are implicit and are essentially deduced from relative preferences over different model outputs. In this work, instead of relying on human feedback, we introduce the design of reward functions that explicitly encode core human values for Reinforcement Learning-based fine-tuning of foundation agent models. Specifically, we use intrinsic rewards for the moral alignment of LLM agents. We evaluate our approach using the traditional philosophical frameworks of Deontological Ethics and Utilitarianism, quantifying moral rewards for agents in terms of actions and consequences on the Iterated Prisoner's Dilemma (IPD) environment. We also show how moral fine-tuning can be deployed to enable an agent to unlearn a previously developed selfish strategy. Finally, we find that certain moral strategies learned on the IPD game generalize to several other matrix game environments. In summary, we demonstrate that fine-tuning with intrinsic rewards is a promising general solution for aligning LLM agents to human values, and it might represent a more transparent and cost-effective alternative to currently predominant alignment techniques.

Problem

• Ethical Alignment
• Large Language Models
• Moral Values

Innovation

• Moral Reinforcement Learning
• Ethical Alignment
• Generalizable Moral Strategies