{"ID":2854291,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.16175","arxiv_id":"2510.16175","title":"The Formalism-Implementation Gap in Reinforcement Learning Research","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.16175","url_pdf":"https://arxiv.org/pdf/2510.16175v2","authors":"[\"Pablo Samuel Castro\"]","published":"2025-10-17T19:35:54Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}