Some considerations on learning to explore via meta-reinforcement learning

Large-scale study of curiosity-driven learning

Can In-Context Learning Support Intrinsic Curiosity?

arXiv:2606. 19476v1 Announce Type: cross Abstract: Effective machine learning depends not only on how we model data, but also on what data we choose to collect.

When to Plan: Learning to Select Between Reactive Control and Deliberative Planning

arXiv:2607. 16421v1 Announce Type: new Abstract: It has long been recognized that humans have the ability to switch between fast, reactive decision-making and slower, deliberative planning.

Clearing the Fog: Towards Installing and Refining Proactive Exploration Capabilities in LLM Agents

arXiv:2608. 14339v1 Announce Type: new Abstract: We study proactive exploration in LLM agents, i.

Plan online, learn offline: Efficient learning and exploration via model-based control

Can In-Context Learning Support Intrinsic Curiosity?

Effective machine learning depends not only on how we model data, but also on what data we choose to collect. While large sequence models have revolutionized data modeling, the problem of automated data selection, or "intrinsic curiosity", remains a significant challenge.