[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85529-en":3,"doc-seo-85529-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},85529,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Dynamics of Learning under User Choice: Overspecialization and Peer-Model Probing","In machine learning markets, multiple platforms receive data from the same user pool, and users choose the platform that best fits them. Existing work studies only local learner losses and misses feedback effects. Learners optimizing for their current user base can converge to models with arbitrarily poor global performance via an overspecialization trap. The paper introduces a peer-probing algorithm using peer predictions to learn about unobserved users and analyzes when probing restores bounded global risk, confirmed by semi-synthetic experiments on MovieLens, Census, and Amazon Sentiment.","arXiv :2602 .23565v2 [ cs .LG] 11 Jul 2026  \nDynamics of Learning under User Choice: Overspecialization and  \nPeer-Model Probing  \nAdhyyan Narang† Sarah Dean‡ Lillian J. Ratliff† Maryam Fazel†  \nElectrical and Computer Engineering, University of Washington† Computer Science, Cornell University ‡  \nCorrespondence: [adhyyan@uw.edu](adhyyan@uw.edu)  \nAbstract  \nIn many economically relevant contexts where machine learning is deployed, multiple platforms obtain data from the same pool of users, each of whom selects the platform that best serves them. Prior work in this setting focuses exclusively on the “local” losses of learners on the distribution of data that they observe. We find that there exist instances where learners who use existing algorithms almost surely converge to models with arbitrarily poor global performance, even when models with low full-population loss exist. This happens through a feedback-induced mechanism, which we call the overspecialization trap: as learners optimize for users who already prefer them, they become less attractive to users outside this base, which further restricts the data they observe. Inspired by the recent use of knowledge distillation in modern ML, we propose an algorithm that allows learners to \"probe\" the predictions of peer models, enabling them to learn about users who do not select them. Our analysis characterizes when probing succeeds: this procedure converges almost surely to a stationary point with bounded full-population risk when probing sources are sufficiently informative, e.g., a known market leader or a majority of peers with good global performance. We verify our findings with semi-synthetic experiments on the MovieLens, Census, and Amazon Sentiment datasets.1  \n1 Introduction  \nTraditional supervised learning theory typically assumes a single learner observing data drawn from a fixed distribution. However, this assumption is increasingly violated in modern machine learning markets, such as recommendation platforms and large language model (LLM) services. In these ecosystems, multiple learners operate on the same pool of users, and data is not assigned randomly. Instead, users choose which platform to engage with based on how well that platform serves their specific needs or preferences. Consequently, the data distribution observed by a learner is a function of the learner’s own performance and the choices available in the market. This setting is increasingly garnering interest in the machine learning community [9, 14, 16, 45, 47] .  \nThis coupling between model performance and user selection creates a feedback loop. As a learner optimizes for its current user base, it becomes increasingly specialized to that subpopulation. While this minimizes \"local\" loss on observed users, it often degrades performance on the unobserved population, a phenomenon we term overspecialization. Once a learner is overspecialized, it gets caught in an informational trap: it cannot learn to serve new users because it never observes them, and it never observes them because it cannot serve them. At a societal level, this dynamic fuels the formation of algorithmic echo chambers [4 , 13 , 26 , 31], where platforms fragment the population rather than learning a robust, globally capable model.  \nIndependently, another trend has become relevant in modern machine learning systems that has implications for the overspecialization problem: techniques such as knowledge distillation and training on synthetic data are becoming ubiquitous, particularly in the training of Large Language  \n1 Code for this paper is available at: [https://github.com/AdhyyanNarang/overspecialization-probing](https://github.com/AdhyyanNarang/overspecialization-probing).  \nModels [24 , 52] . While these methods are typically employed to improve reasoning capabilities or computational efficiency (through compression of data), they introduce a structural change to the learning dynamic. 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sufficiently informative, such as a known market leader or a majority of peers with good global 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problem does the paper identify in multi-platform machine learning markets?","Question",{"text":75,"@type":76},"Learners can become overspecialized due to a feedback loop between model performance and user selection, causing low local loss but arbitrarily poor global performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does peer-model probing mitigate the overspecialization trap?",{"text":80,"@type":76},"The proposed MSGD-P algorithm mixes updates from organic users with pseudo-labeled queries generated from peer models, allowing learners to gather information about users who would not otherwise select them.",{"name":82,"@type":73,"acceptedAnswer":83},"When does probing successfully restore bounded global competence?",{"text":84,"@type":76},"Probing converges to a stationary point with bounded full-population risk when probing sources are sufficiently informative, such as a known market leader or a majority of peers with good global 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