arXiv Machine Learning June 5, 2026 By Dae Yon Hwang, Raunaq Suri, Valentin Villecroze, Anthony L. Caterini, Jesse C. Cresswell, No\"el Vouitsis, Brendan Leigh Ross

Agentic Monte Carlo: Simulating Reinforcement Learning for Black-Box Agents

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arXiv:2606. 05296v1 Announce Type: new Abstract: LLM agents operate in two distinct regimes: open-weight agents amenable to reinforcement learning (RL) and black-box agents whose behaviour must be controlled purely at test time.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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ClawGym II: Exploring Black-Box RL on Agent Harness

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