Title: OLLM: Options-based Large Language Models

Source: http://arxiv.org/abs/2604.19087v1

Summary:

OLLM introduces a novel architectural modification that replaces standard next-token prediction with a set of learned options indexed by a discrete latent variable, significantly enhancing diversity and controllability. This shift to latent-space policy selection provides a foundational primitive for more efficient, robust reasoning and alignment in generative models.