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Computer Science > Artificial Intelligence arXiv:2611.10719 (cs) [Submitted on 7 Nov 2026] Title:No Second Run: Does RSI Stop at Frontier Pre-training?

Authors:Xiaojie Cai, Jiayi Chen, Junwei Chen, Yu Chen, Sikai Cheng, Jinquan Dai, Siyuan Feng, Bin Fu, Dayuan Fu, Guosheng Gao, Qipeng Guo, Chenwei He, Zhulin Hu, Yaxing Huang, Shuomeng Jia, Mohan Jiang, Keyang Lei, Hongliang Li, Xuefeng Li, Zhaocheng Li, Jifan Lin, Pengfei Liu, Xinghan Liu, Yixiu Liu, Pengrui Lu, Siyuan Lu, Kai Lv, Tiantian Mi, Yang Nan, Yu Qiao, Yiwei Qin, Xipeng Qiu, Junhao Shi, Runzhou Shi, Jiadi Su, Bohao Tang, Hanning Wang, Keqi Wang, Pan Wang, Ran Wang, Shaojing Wang, Yishuo Wang, Zhixin Wang, Shenyu Wu, Muhang Xie, Tianze Xu, Hang Yan, Lvmanshan Ye, Hongche Yu, Junjie Yu, Lantian Zhang, Ruihang Zhang, Bin Zhao, Chenyang Zhou, Zijin Zhou, Wenhong Zhu, Zemu Zhu, Yumin Zhuang, Haoyang Zou View a PDF of the paper titled No Second Run: Does RSI Stop at Frontier Pre-training?

, by Xiaojie Cai and 58 other authors View PDF HTML (experimental) Abstract:Pre-training is where every foundation model begins; bringing recursive self-improvement (RSI) there would let AI improve the process that creates AI.

Yet public RSI work has so far targeted only post-training, which starts from an existing base model, runs trials costing GPU-hours, and returns verifiable scores.

Pre-training breaks all three conditions at orders-of-magnitude higher cost: a run consumes hundreds of thousands of GPU-hours over months and cannot be repeated; its objective, post-training headroom, cannot be read off any single score; and what must improve is not a model but the whole production system, from data pipelines and training infrastructure to the teams that run them.

To our knowledge, we are the first to apply RSI to an entire frontier pre-training run from scratch and to report it in detail.

RSI does not stop at frontier pre-training, but it changes form: since no end-to-end loop can close over a single unrepeatable run, we decompose it into smaller loops that can, each repairing a step the full run breaks.The result is 801-30B-A3B-Base, a 30B-A3B MoE trained from scratch on 11.

8T tokens in three months by a newly formed team.We release it as a base model; the lightweight SFT reported here (6B tokens, no RL) only probes post-training headroom.As a base model, 801-30B-A3B outperforms Qwen3-30B-A3B and Nemotron-3.5-Lightning-30B-A3B, trailing only the larger Qwen3.

5-35B-A3B, and keeps this position under identical SFT; with lightweight SFT alone, it achieves the highest average on four agentic benchmarks against fully post-trained models, including Qwen3.6-35B-A3B.

Since post-training RSI presupposes a strong base model, bringing RSI into pre-training acts on the foundation every later stage builds on.We open-source 801-30B-A3B-Base and the lightweight SFT data at this https URL .Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2611.10719 [cs.

AI] (or arXiv:2611.10719v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2611.

10719 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Tiantian Mi [view email] [v1] Sat, 7 Nov 2026 13:26:27 UTC (7,318 KB) Full-text links: Access Paper: View a PDF of the paper titled No Second Run: Does RSI Stop at Frontier Pre-training?

, by Xiaojie Cai and 58 other authorsView PDFHTML (experimental)TeX Source view license Additional Features Audio Summary Current browse context: cs.

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