--- title: A Bitter Lesson for Data Filtering type: article tags: [reinforcement-learning, skill-curation, self-evolving-agents, llm-agent, skill-repo, grpo, composite-rewards] source: newsletter source_url: https://arxiv.org/abs/2605.19407 sha256: 815e57d28df6 ingested: 2026-05-21 --- Markdown Content: # [2605.19407] A Bitter Lesson for Data Filtering [Skip to main content](https://arxiv.org/abs/2605.19407#content) [![Image 1: Cornell University Logo](https://arxiv.org/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg)](https://www.cornell.edu/) [Learn about arXiv becoming an independent nonprofit.](https://tech.cornell.edu/arxiv/) We gratefully acknowledge support from the Simons Foundation, [member institutions](https://info.arxiv.org/about/ourmembers.html), and all contributors.[Donate](https://info.arxiv.org/about/donate.html) [](https://arxiv.org/IgnoreMe) [![Image 2: arxiv logo](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-one-color-white.svg)](https://arxiv.org/)>[cs](https://arxiv.org/list/cs/recent)> arXiv:2605.19407 [Help](https://info.arxiv.org/help) | [Advanced Search](https://arxiv.org/search/advanced) Search [![Image 3: arXiv logo](https://arxiv.org/static/browse/0.3.4/images/arxiv-logomark-small-white.svg)](https://arxiv.org/) [![Image 4: Cornell University Logo](https://arxiv.org/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg)](https://www.cornell.edu/) GO ## quick links * [Login](https://arxiv.org/login) * [Help Pages](https://info.arxiv.org/help) * [About](https://info.arxiv.org/about) # Computer Science > Machine Learning **arXiv:2605.19407** (cs) [Submitted on 19 May 2026] # Title:A Bitter Lesson for Data Filtering Authors:[Christopher Mohri](https://arxiv.org/search/cs?searchtype=author&query=Mohri,+C), [John Duchi](https://arxiv.org/search/cs?searchtype=author&query=Duchi,+J), [Tatsunori Hashimoto](https://arxiv.org/search/cs?searchtype=author&query=Hashimoto,+T) View a PDF of the paper titled A Bitter Lesson for Data Filtering, by Christopher Mohri and 2 other authors [View PDF](https://arxiv.org/pdf/2605.19407)[HTML (experimental)](https://arxiv.org/html/2605.19407v1) > Abstract:We investigate data filtering for large model pretraining via new scaling studies that target the high compute, data-scarce regime. In spite of an apparently common belief that filtering data to include only high-quality information is essential, our experiments suggest that with enough compute, the best data filter is no data filter. We find that sufficiently trained large parameter models not only tolerate low-quality and distractor data, but in fact benefit from nominally ``poor'' data. Subjects:Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as:[arXiv:2605.19407](https://arxiv.org/abs/2605.19407) [cs.LG] (or [arXiv:2605.19407v1](https://arxiv.org/abs/2605.19407v1) [cs.LG] for this version) [https://doi.org/10.48550/arXiv.2605.19407](https://doi.org/10.48550/arXiv.2605.19407) Focus to learn more arXiv-issued DOI via DataCite (pending registration) ## Submission history From: Christopher Mohri [[view email](https://arxiv.org/show-email/d88db0b3/2605.19407)] **[v1]** Tue, 19 May 2026 06:02:36 UTC (458 KB) [](https://arxiv.org/abs/2605.19407)Full-text links: ## Access Paper: View a PDF of the paper titled A Bitter Lesson for Data Filtering, by Christopher Mohri and 2 other authors * [View PDF](https://arxiv.org/pdf/2605.19407) * [HTML (experimental)](https://arxiv.org/html/2605.19407v1) * [TeX Source](https://arxiv.org/src/2605.19407) [![Image 5: license icon](https://arxiv.org/icons/licenses/by-4.0.png)view license](http://creativecommons.org/licenses/by/4.0/ "Rights to this article") ### Current browse context: cs.LG [](https://arxiv.org/prevnext?id=2605.19407&function=next&context=cs.LG "next in cs.LG (accesskey n)") [new](https://arxiv.org/list/cs.LG/new) | [recent](https://arxiv.org/list/cs.LG/recent) | [2026-05](https://arxiv.org/list/cs.LG/2026-05) Change to browse by: [cs](https://arxiv.org/abs/2605.19407?context=cs) [cs.AI](https://arxiv.org/abs/2605.19407?context=cs.AI) ### References & Citations * [NASA ADS](https://ui.adsabs.harvard.edu/abs/arXiv:2605.19407) * [Google Scholar](https://scholar.google.com/scholar_lookup?arxiv_id=2605.19407) * [Semantic Scholar](https://api.semanticscholar.org/arXiv:2605.19407) export BibTeX citation Loading... ## BibTeX formatted citation × Data provided by: [](https://arxiv.org/abs/2605.19407) ### Bookmark [![Image 6: BibSonomy](https://arxiv.org/static/browse/0.3.4/images/icons/social/bibsonomy.png)](http://www.bibsonomy.org/BibtexHandler?requTask=upload&url=https://arxiv.org/abs/2605.19407&description=A%20Bitter%20Lesson%20for%20Data%20Filtering "Bookmark on BibSonomy")[![Image 7: Reddit](https://arxiv.org/static/browse/0.3.4/images/icons/social/reddit.png)](https://reddit.com/submit?url=https://arxiv.org/abs/2605.19407&title=A%20Bitter%20Lesson%20for%20Data%20Filtering "Bookmark on Reddit") Bibliographic Tools # Bibliographic and Citation Tools - [x] Bibliographic Explorer Toggle Bibliographic Explorer _([What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))_ - [x] Connected Papers Toggle Connected Papers _([What is Connected Papers?](https://www.connectedpapers.com/about))_ - [x] Litmaps Toggle Litmaps _([What is Litmaps?](https://www.litmaps.co/))_ - [x] scite.ai Toggle scite Smart Citations _([What are Smart Citations?](https://www.scite.ai/))_ Code, Data, Media # Code, Data and Media Associated with this Article - [x] alphaXiv Toggle alphaXiv _([What is alphaXiv?](https://alphaxiv.org/))_ - [x] Links to Code Toggle CatalyzeX Code Finder for Papers _([What is CatalyzeX?](https://www.catalyzex.com/))_ - [x] DagsHub Toggle DagsHub _([What is DagsHub?](https://dagshub.com/))_ - [x] GotitPub Toggle Gotit.pub _([What is GotitPub?](http://gotit.pub/faq))_ - [x] Huggingface Toggle Hugging Face _([What is Huggingface?](https://huggingface.co/huggingface))_ - [x] ScienceCast Toggle ScienceCast _([What is ScienceCast?](https://sciencecast.org/welcome))_ Demos # Demos - [x] Replicate Toggle Replicate _([What is Replicate?](https://replicate.com/docs/arxiv/about))_ - [x] Spaces Toggle Hugging Face Spaces _([What is Spaces?](https://huggingface.co/docs/hub/spaces))_ - [x] Spaces Toggle TXYZ.AI _([What is TXYZ.AI?](https://txyz.ai/))_ Related Papers # Recommenders and Search Tools - [x] Link to Influence Flower Influence Flower _([What are Influence Flowers?](https://influencemap.cmlab.dev/))_ - [x] Core recommender toggle CORE Recommender _([What is CORE?](https://core.ac.uk/services/recommender))_ - [x] IArxiv recommender toggle IArxiv Recommender _([What is IArxiv?](https://iarxiv.org/about))_ * [Author](https://arxiv.org/abs/2605.19407) * [Venue](https://arxiv.org/abs/2605.19407) * [Institution](https://arxiv.org/abs/2605.19407) * [Topic](https://arxiv.org/abs/2605.19407) About arXivLabs # arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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