{"ID":2838003,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19496","arxiv_id":"2511.19496","title":"Xmodel-2.5: 1.3B Data-Efficient Reasoning SLM","abstract":"Large language models deliver strong reasoning and tool-use skills, yet their computational demands make them impractical for edge or cost-sensitive deployments. We present \\textbf{Xmodel-2.5}, a 1.3-billion-parameter small language model designed as a \\emph{drop-in agent core}. Training with maximal-update parameterization ($μ$P) allows hyper-parameters tuned on a 20M-parameter proxy to transfer directly to the full model, even under the parameter-tied \\emph{tie-word-embedding} architecture. A 1.4T-token Warmup--Stable--Decay curriculum is used, and we further show that \\textbf{switching from AdamW to Muon during the decay phase} improves the 13-task reasoning average by 4.58\\,\\% while keeping every other hyper-parameter fixed, verifying that early AdamW stability can be paired with late Muon sharpening for better downstream performance. FP8-mixed-precision training balances accuracy and throughput. All checkpoints, recipes, and evaluation code are released under the Apache-2.0 license.\\footnote{https://huggingface.co/XiaoduoAILab/Xmodel-2.5 and https://huggingface.co/XiaoduoAILab/Xmodel-2.5-history (training checkpoints).} Training code and evaluation harness: https://github.com/XiaoduoAILab/Xmodel-2.5.","short_abstract":"Large language models deliver strong reasoning and tool-use skills, yet their computational demands make them impractical for edge or cost-sensitive deployments. We present \\textbf{Xmodel-2.5}, a 1.3-billion-parameter small language model designed as a \\emph{drop-in agent core}. Training with maximal-update parameteriz...","url_abs":"https://arxiv.org/abs/2511.19496","url_pdf":"https://arxiv.org/pdf/2511.19496v1","authors":"[\"Yang Liu\",\"Xiaolong Zhong\",\"Ling Jiang\"]","published":"2025-11-23T13:00:47Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false,"code_links":[{"ID":606727,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2838003,"paper_url":"https://arxiv.org/abs/2511.19496","paper_title":"Xmodel-2.5: 1.3B Data-Efficient Reasoning SLM","repo_url":"https://github.com/XiaoduoAILab/Xmodel-2.5","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}