CoFrGeNet: Continued Fraction Architectures for Language Generation

• URL: http://arxiv.org/abs/2601.21766v2
• Date: Fri, 30 Jan 2026 13:45:39 GMT
• Title: CoFrGeNet: Continued Fraction Architectures for Language Generation
• Authors: Amit Dhurandhar, Vijil Chenthamarakshan, Dennis Wei, Tejaswini Pedapati, Karthikeyan Natesan Ramamurthy, Rahul Nair,
• Abstract summary: We introduce a new function class for generative modeling inspired by continued fractions.<n>We design novel architectural components based on this function class that can replace Multi-head Attention and Feed-Forward Networks in Transformer blocks.<n>Our components are a plug-in replacement requiring little change in training or inference procedures.
• Score: 36.20981075573288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transformers are arguably the preferred architecture for language generation. In this paper, inspired by continued fractions, we introduce a new function class for generative modeling. The architecture family implementing this function class is named CoFrGeNets - Continued Fraction Generative Networks. We design novel architectural components based on this function class that can replace Multi-head Attention and Feed-Forward Networks in Transformer blocks while requiring much fewer parameters. We derive custom gradient formulations to optimize the proposed components more accurately and efficiently than using standard PyTorch-based gradients. Our components are a plug-in replacement requiring little change in training or inference procedures that have already been put in place for Transformer-based models thus making our approach easy to incorporate in large industrial workflows. We experiment on two very different transformer architectures GPT2-xl (1.5B) and Llama3 (3.2B), where the former we pre-train on OpenWebText and GneissWeb, while the latter we pre-train on the docling data mix which consists of nine different datasets. Results show that the performance on downstream classification, Q\& A, reasoning and text understanding tasks of our models is competitive and sometimes even superior to the original models with $\frac{2}{3}$ to $\frac{1}{2}$ the parameters and shorter pre-training time. We believe that future implementations customized to hardware will further bring out the true potential of our architectures.

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