Moral Alignment for LLM Agents (2410.01639v2)

Abstract: Decision-making agents based on pre-trained 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.

Moral Alignment for LLM Agents

The research presented in the paper titled "Moral Alignment for LLM Agents" offers an innovative approach to the alignment of decision-making agents based on LLMs. This work by Elizaveta Tennant, Stephen Hailes, and Mirco Musolesi of University College London explores the alignment problem—a critical issue within machine learning, particularly as LLMs increasingly influence human activities.

Problem Statement and Approach

The current methodology for aligning LLMs often relies on Reinforcement Learning from Human Feedback (RLHF) or Direct Preference Optimization (DPO). These rely on implicit representations of human values derived from relative model output preferences, a process that can be both costly and inconsistent. Instead, this paper explores the explicit encoding of intrinsic rewards that represent fundamental human values for fine-tuning foundation agent models through Reinforcement Learning (RL).

Specifically, the research evaluates moral alignment strategies using intrinsic rewards within the frameworks of Deontological Ethics and Utilitarianism. The Iterated Prisoner's Dilemma (IPD) serves as the primary environment for testing these moral alignments, offering quantifiable rewards in terms of actions and outcomes.

Methodology

Fine-tuning LLM agents involves interfacing with specific action tokens within a text-based framework. This paper utilizes a PPO-based fine-tuning method incorporating intrinsic rewards that either discourage defecting against cooperating opponents (Deontological) or promote maximizing collective welfare (Utilitarian).

A key experiment involves dual scenarios: training an LLM agent against both a fixed-strategy opponent (Tit-for-Tat) and another learning LLM agent. This dual setup allows the analysis of consistency and stability in moral strategy formation under different conditions.

Results

The findings reveal that fine-tuning with intrinsic rewards can substantially align LLM agents to predefined moral values. When agents were trained under Deontological norms, they adhered strictly to not defecting against cooperating opponents, showcasing adherence to moral restrictions. Utilitarian training led agents to prioritize mutual cooperation, maximizing joint rewards.

A significant revelation is the potential for LLMs to "unlearn" undesirable, selfish strategies initially developed under game reward conditions. The transition to moral fine-tuning displayed effective shifts to more cooperative behaviors, emphasizing the adaptability of the approach.

Additionally, testing across various matrix games beyond IPD demonstrated that moral strategies could generalize effectively, albeit with some variance based on the intrinsic complexity of different games.

Implications and Future Work

The methodology proposed represents a promising alternative to implicit alignment techniques, potentially offering more transparency and less costly processes. By embedding moral strategies directly into the learning process of LLMs, this approach could mitigate risks associated with latent unwanted behaviors that surface post-deployment.

Further exploration could involve extending intrinsic reward-based methodologies to more complex environments or integrating multiple moral frameworks within a single agent to address pluralistic alignment challenges.

Moreover, the possibility of adapting the approach to various scales of models and applications signifies a broader potential impact for ensuring that future AI systems operate safely and harmoniously within human ethical frameworks.

Conclusion

This paper contributes to the discourse on aligning AI systems with human values, especially as systems grow more autonomous and impactful. By leveraging explicit, intrinsic rewards from well-established moral theories, the research presents a viable path toward more ethically aligned LLM agents, paving the way for future advancements in AI alignment.