ICML 2025

MAGELLAN: Metacognitive predictions of learning progress
guide autotelic LLM agents in large goal spaces
Loris Gaven 1 Thomas Carta 1 Cl´ement Romac 1 2 C´edric Colas 1 3 Sylvain Lamprier 4 Olivier Sigaud 5
Pierre-Yves Oudeyer 1
Abstract
Open-ended learning agents must efficiently prior-
itize goals in vast possibility spaces, focusing on
those that maximize learning progress (LP). When
such autotelic exploration is achieved by LLM
agents trained with online RL in high-dimensional
and evolving goal spaces, a key challenge for LP
prediction is modeling one’s own competence, a
form of metacognitive monitoring. Traditional
approaches either require extensive sampling or
rely on brittle expert-defined goal groupings. We
introduce MAGELLAN, a metacognitive frame-
work that lets LLM agents learn to predict their
competence and LP online. By capturing seman-
tic relationships between goals, MAGELLAN en-
ables sample-efficient LP estimation and dynamic
adaptation to evolving goal spaces through gener-
alization. In an interactive learning environment,
we show that MAGELLAN improves LP predic-
tion efficiency and goal prioritization, being the
only method allowing the agent to fully master
a large and evolving goal space. These results
demonstrate how augmenting LLM agents with
a metacognitive ability for LP predictions can ef-
fectively scale curriculum learning to open-ended
goal spaces.
1. Introduction
Humans are open-ended learners, continuously exploring
and developing new skills through their lifetime. A key
mechanism to enable this remarkable capacity is curiosity-
driven learning — the intrinsic motivation to explore for
1Inria (Flowers), University of Bordeaux, France 2Hugging
Face 3MIT, Computational Cognitive Science Lab, Cambridge,
MA, USA 4Univ Angers, LERIA, SFR MATHSTIC, F-49000
Angers, France 5Sorbonne Universit´e, ISIR, Paris, France. Corre-
spondence to: Loris Gaven <loris.gaven@inria.fr>.
Proceedings of the 42 nd International Conference on Machine
Learnin...