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Recently, modern machine learning (ML), and specifically reinforcement learning (RL), have gained increased attention as a means to develop a query optimizer (QO). In this work, we take a closer look at two recent RL-based QO methods to better understand their behavior. We find that these RL-based methods do not generalize as well as it seems at first glance. Thus, we ask a simple question: <jats:italic>How do RL-based QOs compare to a simple, modern, adaptive query processing approach?<\/jats:italic> To answer this question, we chose two simple adaptive query processing techniques and implemented them in PostgreSQL. The first adapts an individual join operation on-the-fly and switches between a Nested Loop Join algorithm and a Hash Join algorithm to avoid sub-optimal join algorithm decisions. The second is a technique called <jats:italic>Lookahead Information Passing<\/jats:italic> (LIP), in which adaptive semijoin techniques are used to make a pipeline of join operations execute efficiently. To our surprise, we find that this simple adaptive query processing approach is not only competitive to these RL-based approaches but, in some cases, outperforms the RL-based approaches. The adaptive approach is also appealing because it does not require an expensive training step, and it is fully interpretable compared to the RL-based QO approaches. Further, the adaptive method works across complex query constructs that RL-based QO methods currently cannot optimize.<\/jats:p>","DOI":"10.1007\/s00778-025-00936-6","type":"journal-article","created":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T08:07:58Z","timestamp":1756541278000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7157-156X","authenticated-orcid":false,"given":"Yunjia","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Yannis","family":"Chronis","sequence":"additional","affiliation":[]},{"given":"Jignesh M.","family":"Patel","sequence":"additional","affiliation":[]},{"given":"Theodoros","family":"Rekatsinas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,30]]},"reference":[{"issue":"1","key":"936_CR1","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1145\/3542700.3542703","volume":"51","author":"R Marcus","year":"2022","unstructured":"Marcus, R., Negi, P., Mao, H., Tatbul, N., Alizadeh, M., Kraska, T.: Bao: Making learned query optimization practical. 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