Introspective X Training: Feedback Conditioning Improves Scaling Across all LLM Training Stages
A new method called Introspective X Training (IXT) has been proposed to enhance the efficiency of large language model (LLM) training. This approach utilizes feedback conditioning to improve scaling across various training stages. Experiments indicate that IXT can significantly increase compute efficiency and achieve superior performance in specific domains like math and code.
- ▪Introspective X Training (IXT) is designed to improve scaling efficiency in LLM training pipelines.
- ▪The method employs a feedback conditioning model to annotate data with critiques, enhancing training quality from the start.
- ▪Comprehensive experiments show that IXT can achieve up to 2.8 times more compute efficiency and better performance in challenging domains.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20285 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
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Computer Science > Machine Learning arXiv:2605.20285 (cs) [Submitted on 19 May 2026] Title:Introspective X Training: Feedback Conditioning Improves Scaling Across all LLM Training Stages Authors:Brandon Cui, Ximing Lu, Jaehun Jung, Syeda Nahida Akter, Hyunwoo Kim, Yuxiao Qu, David Acuna, Shrimai Prabhumoye, Yejin Choi, Prithviraj Ammanabrolu View a PDF of the paper titled Introspective X Training: Feedback Conditioning Improves Scaling Across all LLM Training Stages, by Brandon Cui and 9 other authors View PDF HTML (experimental) Abstract:We tackle the question of how to scale more efficiently across the many, ever-growing stages of current LLM training pipelines. Our guiding intuition stems from the fact that the dynamics of later stages of the pipeline, e.g.
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