Co-evolving Policies and Values for Generalizable and Aligned AI
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2025
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| author | Revista, Zen IA, 10 |
| author_facet | Revista, Zen IA, 10 |
| contents | The development of Artificial Intelligence (AI) systems capable of robust generalization across diverse tasks and environments, while remaining reliably aligned with human values, represents a paramount challenge in contemporary AI research. Traditional approaches often specify values statically or learn them through fixed reward functions, which can lead to brittle alignment and poor generalization when faced with novel situations or evolving human expectations. This paper proposes a novel framework for achieving generalizable and aligned AI through the co-evolution of policies and values. Our methodology integrates reinforcement learning for policy optimization with a dynamic value learning mechanism, where policies are shaped by evolving value functions, and conversely, value functions are refined based on observed policy outcomes and interactions within diverse environments. This reciprocal relationship allows the AI system to adapt its behavioral strategies while simultaneously developing a more nuanced and contextually aware understanding of underlying ethical principles. We demonstrate through theoretical exposition and hypothetical results that such a co-evolutionary process can lead to AI agents that not only perform effectively across a wide range of tasks but also exhibit more robust, adaptable, and ethically aligned behavior compared to systems with static value specifications. The discussion explores the implications, limitations, and future directions for this approach, highlighting its potential to foster the development of truly intelligent and responsible AI. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17793275 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Co-evolving Policies and Values for Generalizable and Aligned AI Revista, Zen IA, 10 The development of Artificial Intelligence (AI) systems capable of robust generalization across diverse tasks and environments, while remaining reliably aligned with human values, represents a paramount challenge in contemporary AI research. Traditional approaches often specify values statically or learn them through fixed reward functions, which can lead to brittle alignment and poor generalization when faced with novel situations or evolving human expectations. This paper proposes a novel framework for achieving generalizable and aligned AI through the co-evolution of policies and values. Our methodology integrates reinforcement learning for policy optimization with a dynamic value learning mechanism, where policies are shaped by evolving value functions, and conversely, value functions are refined based on observed policy outcomes and interactions within diverse environments. This reciprocal relationship allows the AI system to adapt its behavioral strategies while simultaneously developing a more nuanced and contextually aware understanding of underlying ethical principles. We demonstrate through theoretical exposition and hypothetical results that such a co-evolutionary process can lead to AI agents that not only perform effectively across a wide range of tasks but also exhibit more robust, adaptable, and ethically aligned behavior compared to systems with static value specifications. The discussion explores the implications, limitations, and future directions for this approach, highlighting its potential to foster the development of truly intelligent and responsible AI. |
| title | Co-evolving Policies and Values for Generalizable and Aligned AI |
| url | https://doi.org/10.5281/zenodo.17793275 |