Moral Alignment for LLM Agents
Elizaveta Tennant, Stephen Hailes, Mirco Musolesi
TL;DR
This paper proposes a transparent method for aligning large language model agents with human moral values by using explicit intrinsic reward functions based on ethical frameworks, evaluated in game environments.
Contribution
It introduces a novel approach to moral alignment of LLM agents through explicit reward functions, moving away from implicit preference-based methods.
Findings
Moral reward functions can effectively align LLM agents with human values.
Agents trained with moral rewards can unlearn selfish strategies.
Moral strategies learned in one environment can generalize to others.
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 underway to develop more generalist agents. As LLM-based systems become more agentic, their influence on human activity will grow and their transparency 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, opaque 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 and transparently encode core human values for Reinforcement Learning-based…
Peer Reviews
Decision·ICLR 2025 Poster
This is a fantastic paper! It addresses a critical and highly understudied problem with a clever and interesting solution, the experiments are clear, simple, and well-executed and the writing is direct and easy to follow.
Though I love what the authors are doing here, a potential weakness of this approach, is encapsulated by this sentence in the introduction: "In theory, our solution can be applied to any situation in which one can define a payoff matrix that captures the choices available to an agent that have moral implications." But of course the trouble is that the world is very messy and complex place that often cannot be easily captured in a payoff matrix. Now, the reason that economic games have gotten s
- LLM moral alignment is an important topic, and especially the study of LLM agents in strategic interactions. - I was able to follow along with the experimental methodology and results and they seem sound to me. - Defining rewards intrinsic to games and training on these is an interesting idea that could help with alignment and avoid problems with inadequacies of human feedback. Moreover, game rewards might scale better than collecting human data. - The authors do a great job visualizing the mo
- As far as I can tell, the deontological rewards are hand-designed. As such I don't currently see how this approach could be scaled to more games with more complex action spaces. Moreover, in the specific ipd setting, deontological rewards basically amount to specifically training the LLM to implement a tit-for-tat policy. The fact that the model can learn this policy when specifically trained on hand crafted rewards is not that interesting. - When it comes to utilitarian rewards, these are not
1. Aligning LLM agents to human values is a highly relevant and important research area. 2. The idea is interesting and I have no issues with any of the experiments. The authors do a good job of identifying the limitations of their method themselves, and other than those this is well-written research. Experimental results are thorough and comprehensive, and well cover key concerns I had as I read (e.g. anonymized names of action tokens, randomizing the action token order, etc). The paper is well
As mentioned above, my main concerns here have been pointed out by the authors themselves already. Namely: 1. “A limitation of this approach is that it requires the specification of rewards for a particular environment”, and 2. “An extension of this method to other environments would be of great interest, including fine-tuning agents with other payoff structures, more complex games or longer history lengths” My primary concern is point 2; in my opinion, as-is this is a slightly weak paper and
Videos
Taxonomy
TopicsDigital Rights Management and Security · Auction Theory and Applications · Blockchain Technology Applications and Security