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        "description": "Token/data selection has predecessors. Reference-model training/scoring costs remain; fewer selected training tokens are not automatically equivalent end-to-end compute savings. Unrelated to Reka's2026Rho-1 multimodal system; no grading/ranking scores assigned by WRS.",
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      "statement": "# Microsoft Rho-1: token-selective language-model pretraining\n\nScored corpus tokens with a reference model and focused training loss on higher excess-loss tokens, evaluating token-selection effects in math and general language-model training.\n\n## Scientific scope\n\nToken/data selection has predecessors. Reference-model training/scoring costs remain; fewer selected training tokens are not automatically equivalent end-to-end compute savings. Unrelated to Reka's2026Rho-1 multimodal system; no grading/ranking scores assigned by WRS.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nZhenghao Lin; Zhibin Gou; Yeyun Gong; Xiao Liu; Yelong Shen; Ruochen Xu; Chen Lin; Yujiu Yang; Jian Jiao; Nan Duan; Weizhu Chen\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Rho-1: Not All Tokens Are What You Need](https://arxiv.org/abs/2404.07965v1)\n",
      "summary": "Scored corpus tokens with a reference model and focused training loss on higher excess-loss tokens, evaluating token-selection effects in math and general language-model training.",
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