The Formalism-Implementation Gap in Reinforcement Learning Research

• URL: http://arxiv.org/abs/2510.16175v2
• Date: Tue, 28 Oct 2025 14:06:41 GMT
• Title: The Formalism-Implementation Gap in Reinforcement Learning Research
• Authors: Pablo Samuel Castro,
• Abstract summary: This paper argues that reinforcement learning research should stop focusing solely on demonstrating agent capabilities.<n>It argues that we need to be more precise on how our benchmarks map to the underlying mathematical formalisms.
• Score: 17.922282609849184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The last decade has seen an upswing in interest and adoption of reinforcement learning (RL) techniques, in large part due to its demonstrated capabilities at performing certain tasks at "super-human levels". This has incentivized the community to prioritize research that demonstrates RL agent performance, often at the expense of research aimed at understanding their learning dynamics. Performance-focused research runs the risk of overfitting on academic benchmarks -- thereby rendering them less useful -- which can make it difficult to transfer proposed techniques to novel problems. Further, it implicitly diminishes work that does not push the performance-frontier, but aims at improving our understanding of these techniques. This paper argues two points: (i) RL research should stop focusing solely on demonstrating agent capabilities, and focus more on advancing the science and understanding of reinforcement learning; and (ii) we need to be more precise on how our benchmarks map to the underlying mathematical formalisms. We use the popular Arcade Learning Environment (ALE; Bellemare et al., 2013) as an example of a benchmark that, despite being increasingly considered "saturated", can be effectively used for developing this understanding, and facilitating the deployment of RL techniques in impactful real-world problems.

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