Agent-centric learning: from external reward maximization to internal knowledge curation
Abstract
The pursuit of general intelligence has traditionally centered on external objectives: an agent's control over its environments or mastery of specific tasks. This external focus, however, can produce specialized agents that lack adaptability. We propose representational empowerment, a new perspective towards a truly agent-centric learning paradigm by moving the locus of control inward. This objective measures an agent's ability to controllably maintain and diversify its own knowledge structures. We posit that the capacity -- to shape one's own understanding -- is an element for achieving better ``preparedness'' distinct from direct environmental influence. Focusing on internal representations as the main substrate for computing empowerment offers a new lens through which to design adaptable intelligent systems.
Cite
@article{arxiv.2507.22255,
title = {Agent-centric learning: from external reward maximization to internal knowledge curation},
author = {Hanqi Zhou and Fryderyk Mantiuk and David G. Nagy and Charley M. Wu},
journal= {arXiv preprint arXiv:2507.22255},
year = {2025}
}
Comments
RLC Finding the Frame Workshop 2025