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

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cs.LG2025

HERAKLES: Hierarchical Skill Compilation for Open-ended LLM Agents

Thomas Carta, Clément Romac, Loris Gaven +3

We study goal-conditioned reinforcement learning in partially observable environments with sparse rewards and large, structured goal spaces. In such settings, complex goals often r…

cs.LG2024

SAC-GLAM: Improving Online RL for LLM agents with Soft Actor-Critic and Hindsight Relabeling

Loris Gaven, Clement Romac, Thomas Carta +3

The past years have seen Large Language Models (LLMs) strive not only as generative models but also as agents solving textual sequential decision-making tasks. When facing complex…