Position: RL Researchers Need to Distinguish Between Solving Simulators and Using Simulators as a Proxy
This position paper argues that reinforcement learning researchers often conflate two distinct uses of simulators: solving the simulator as an end in itself, and using it as a proxy for real-world deployment. Through experiments and examples, the authors show that failing to distinguish these settings can lead to misleading conclusions, and call for clearer empirical practices.
[2606.28433] Position: RL Researchers Need to Distinguish Between Solving Simulators and Using Simulators as a Proxy
[Submitted on 26 Jun 2026]
Title:Position: RL Researchers Need to Distinguish Between Solving Simulators and Using Simulators as a Proxy
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Abstract:One goal in reinforcement learning (RL) research is to understand general-purpose sequential decision-making, using benchmark simulators as a proxy for learning in deployment settings. When running experiments, however, the goal of achieving high performance in the simulator can mutate into focusing exclusively on solving the simulator. To achieve high scores, researchers may adopt solutions exclusively meant for solving simulators, rather than learning while the agent is deployed outside a simulator. Solving simulators is also worthy of investigation, but it is a fundamentally different RL research question. In this paper, we argue that RL researchers need to distinguish between two use cases of simulators: solving simulators and using simulators as a proxy for learning in deployment. We first discuss how these two use-cases are importantly different, in terms of constraints on how the agent can use the simulator, which algorithms are appropriate, and which evaluation metrics are appropriate. We then highlight several issues and misleading conclusions that can occur by not making the distinction between these two settings clear, supported with examples and simple experiments. This work is a call to the community to begin clearly distinguishing how they are using simulators in their work, hopefully sparking further discussion on which empirical practices work best in each setting.
Comments: This work has been accepted at the ICML 2026 position paper track. The peer reviewed reference is provided in the public OpenReview page at this https URL Additionally the publication can be seen at this link : this https URL
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2606.28433 [cs.LG]
(or arXiv:2606.28433v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.28433
arXiv-issued DOI via DataCite
Journal reference: ICML, 2026
Submission history
From: Matthew Vandergrift [view email] [v1] Fri, 26 Jun 2026 03:19:27 UTC (369 KB)
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