Position: RL Researchers Need to Distinguish Between Solving Simulators and Using Simulators as a Proxy
arXiv:2606.28433v1 Announce Type: new 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 investig
The proliferation of sophisticated AI models and simulators necessitates a clearer demarcation between research goals focused on benchmark performance versus real-world applicability.
This paper highlights a critical potential misalignment in AI research, where progress might be measured against simulated environments rather than practical deployment, impacting the real-world utility of advanced AI.
Increased awareness of this distinction could lead to a re-evaluation of research methodologies and success metrics in reinforcement learning, shifting focus towards deployable solutions rather than purely theoretical or simulated achievements.
- · AI deployment platforms
- · Real-world AI application developers
- · Researchers focused solely on simulation benchmarks
- · AI projects with limited real-world transferability
AI research will recalibrate its focus towards challenges inherent in deployment settings.
New benchmarks and methodologies will emerge that more accurately reflect real-world performance and generalizability.
This could accelerate the integration of AI agents into complex physical and operational environments, driving broader adoption.
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Read at arXiv cs.LG