Philosophies 2026, 11(4), 112; https://doi.org/10.3390/philosophies11040112 - 6 Jul 2026
Abstract
This study conducts a technical analysis of frontier generative AI algorithms—including Meta’s Self-Rewarding Language Models, DeepMind’s EVA (Evolving Alignment via Asymmetric Self-Play) framework, and DeepSeek’s pure reinforcement-learning models—in order to examine an intrinsic paradigm shift in the ethical governance of generative artificial intelligence
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This study conducts a technical analysis of frontier generative AI algorithms—including Meta’s Self-Rewarding Language Models, DeepMind’s EVA (Evolving Alignment via Asymmetric Self-Play) framework, and DeepSeek’s pure reinforcement-learning models—in order to examine an intrinsic paradigm shift in the ethical governance of generative artificial intelligence and to advance a physicalist analysis of algorithmic endogenous ethics. Combining a close reading of alignment techniques (RLHF, DPO, iterative DPO, GRPO) with a conceptual analysis grounded in Peter-Paul Verbeek’s theory of technological mediation and moral materialization, the paper traces how value-alignment goals are being “materialized” into internal, dynamic, and evolvable “moral scripts” within the algorithms themselves. The analysis shows that contemporary alignment practices are moving from external ethical discipline toward endogenous norms generated through iterative self-evaluation, asymmetric self-play, and rule-based self-exploration. The paper argues that this trend warrants a re-examination of Verbeek’s framework for its capacity to explain the co-evolution of technology and morality in the digital age, and it envisions a future of human–machine value co-evolution organized around new research directions such as “Setting as Governance” and “value homeostasis mechanisms”.
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(This article belongs to the Special Issue Phenomenological Philosophy of Science and Technology)
