most citedMAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces

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cs.AI2025

Optimizing for Persuasion Improves LLM Generalization: Evidence from Quality-Diversity Evolution of Debate Strategies

Aksel Joonas Reedi, Corentin Léger, Julien Pourcel +3

Large Language Models (LLMs) optimized to output truthful answers often overfit, producing brittle reasoning that fails to generalize. While persuasion-based optimization has shown…

cs.AI2025★ 1 cited

MAGELLAN: Metacognitive predictions of learning progress guide autotelic LLM agents in large goal spaces

Loris Gaven, Thomas Carta, Clément Romac +4

Open-ended learning agents must efficiently prioritize goals in vast possibility spaces, focusing on those that maximize learning progress (LP). When such autotelic exploration is…