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Donate arxiv logo > cs > arXiv:2510.15061 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Machine Learning arXiv:2510.15061 (cs) [Submitted on 16 Oct 2025 (v1), last revised 21 Oct 2025 (this version, v2)] Title:Antislop: A Comprehensive Framework for Identifying and Eliminating Repetitive Patterns in Language Models Authors:Samuel Paech, Allen Roush, Judah Goldfeder, Ravid Shwartz-Ziv View a PDF of the paper titled Antislop: A Comprehensive Framework for Identifying and Eliminating Repetitive Patterns in Language Models, by Samuel Paech and 3 other authors View PDF Abstract:Widespread LLM adoption has introduced characteristic repetitive phraseology, termed "slop," which degrades output quality and makes AI-generated text immediately recognizable. We present Antislop, a comprehensive framework providing tools to both detect and eliminate these overused patterns. Our approach combines three innovations: (1) The Antislop Sampler, which uses backtracking to suppress unwanted strings at inference time without destroying vocabulary; (2) An automated pipeline that profiles model-specific slop against human baselines and generates training data; (3) Final Token Preference Optimization (FTPO), a novel fine-tuning method that operates on individual tokens, surgically adjusting logits wherever a banned pattern has appeared in an inference trace. We demonstrate that some slop patterns appear over 1,000x more frequently in LLM output than human text. The Antislop Sampler successfully suppresses 8,000+ patterns while maintaining quality, whereas token banning becomes unusable at just 2,000. Most importantly, FTPO achieves 90% slop reduction while maintaining or improving performance in cross-domain evals including GSM8K, MMLU, and creative writing tasks. In contrast, DPO suffers significant degradation in writing quality and lexical diversity despite achieving weaker suppression. We release all code and results under MIT license: this https URL. Comments: 11 pages + appendices, 16 figures Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL) Cite as: arXiv:2510.15061 [cs.LG] (or arXiv:2510.15061v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2510.15061 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Samuel Paech [view email] [v1] Thu, 16 Oct 2025 18:22:22 UTC (536 KB) [v2] Tue, 21 Oct 2025 21:42:07 UTC (536 KB) Full-text links: Access Paper: View a PDF of the paper titled Antislop: A Comprehensive Framework for Identifying and Eliminating Repetitive Patterns in Language Models, by Samuel Paech and 3 other authors * View PDF * TeX Source license icon view license Current browse context: cs.LG < prev | next > new | recent | 2025-10 Change to browse by: cs cs.CL References & Citations * NASA ADS * Google Scholar * Semantic Scholar export BibTeX citation Loading... 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