arXiv:2510.16175v2 Announce Type: replace 
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.

يتناول المقال الاهتمام المتزايد بتعلم التعزيز (RL) خلال العقد الماضي، مع تسليط الضوء على أدائه المذهل في مهام مختلفة. ومع ذلك، يثير مخاوف بشأن التركيز على مقاييس الأداء، مما قد يطغى على أهمية فهم الديناميات التعليمية الأساسية لوكلاء RL. وهذا مهم لأن نهجًا متوازنًا يمكن أن يؤدي إلى أنظمة RL أكثر قوة وموثوقية في المستقبل.

El artículo discute el creciente interés en el aprendizaje por refuerzo (RL) en la última década, destacando su impresionante rendimiento en diversas tareas. Sin embargo, plantea preocupaciones sobre el enfoque en las métricas de rendimiento, que puede eclipsar la importancia de comprender las dinámicas de aprendizaje subyacentes de los agentes de RL. Esto es significativo, ya que un enfoque equilibrado podría llevar a sistemas de RL más robustos y confiables en el futuro.

L'article aborde l'intérêt croissant pour l'apprentissage par renforcement (RL) au cours de la dernière décennie, soulignant ses performances impressionnantes dans diverses tâches. Cependant, il soulève des préoccupations concernant l'accent mis sur les indicateurs de performance, qui pourrait éclipser l'importance de comprendre les dynamiques d'apprentissage sous-jacentes des agents RL. Cela est significatif car une approche équilibrée pourrait conduire à des systèmes RL plus robustes et fiables à l'avenir.

The article discusses the growing interest in reinforcement learning (RL) over the past decade, highlighting its impressive performance in various tasks. However, it raises concerns about the focus on performance metrics, which may overshadow the importance of understanding the underlying learning dynamics of RL agents. This is significant as a balanced approach could lead to more robust and reliable RL systems in the future.

The Formalism-Implementation Gap in Reinforcement Learning Research

arXiv:2511.17228v1 Announce Type: cross 
Abstract: Artificial intelligence in dynamic, real-world environments requires the capacity for continual learning. However, standard deep learning suffers from a fundamental issue: loss of plasticity, in which networks gradually lose their ability to learn from new data. Here we show that quantum learning models naturally overcome this limitation, preserving plasticity over long timescales. We demonstrate this advantage systematically across a broad spectrum of tasks from multiple learning paradigms, including supervised learning and reinforcement learning, and diverse data modalities, from classical high-dimensional images to quantum-native datasets. Although classical models exhibit performance degradation correlated with unbounded weight and gradient growth, quantum neural networks maintain consistent learning capabilities regardless of the data or task. We identify the origin of the advantage as the intrinsic physical constraints of quantum models. Unlike classical networks where unbounded weight growth leads to landscape ruggedness or saturation, the unitary constraints confine the optimization to a compact manifold. Our results suggest that the utility of quantum computing in machine learning extends beyond potential speedups, offering a robust pathway for building adaptive artificial intelligence and lifelong learners.

أظهرت التطورات الأخيرة في نماذج التعلم الكمومي قدرتها على الحفاظ على اللدونة في بيئات التعلم المستمر، مما يعالج قيدًا كبيرًا موجودًا في أنظمة التعلم العميق التقليدية. تظهر هذه النماذج قدرات تعلم متسقة عبر مجموعة متنوعة من المهام وأنواع البيانات، بما في ذلك التعلم المراقب والتعلم المعزز.

Los avances recientes en modelos de aprendizaje cuántico han demostrado su capacidad para mantener la plasticidad en entornos de aprendizaje continuo, abordando una limitación significativa que se encuentra en los sistemas de aprendizaje profundo tradicionales. Estos modelos muestran capacidades de aprendizaje consistentes en diversas tareas y tipos de datos, incluidos el aprendizaje supervisado y el aprendizaje por refuerzo.

Des avancées récentes dans les modèles d'apprentissage quantique ont démontré leur capacité à maintenir la plasticité dans des environnements d'apprentissage continu, répondant à une limitation significative des systèmes d'apprentissage profond traditionnels. Ces modèles montrent des capacités d'apprentissage cohérentes à travers diverses tâches et types de données, y compris l'apprentissage supervisé et l'apprentissage par renforcement.

Recent advancements in quantum learning models have demonstrated their ability to maintain plasticity in continual learning environments, addressing a significant limitation found in traditional deep learning systems. These models show consistent learning capabilities across various tasks and data types, including supervised and reinforcement learning.

The Formalism-Implementation Gap in Reinforcement Learning Research

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