ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Software
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1626899
This article is part of the Research TopicArtificial Intelligence for Software Engineering: Advances, Applications, and ImplicationsView all articles
Exploring the Impact of Fixed Theta Values in RoPE on Character-Level Language Model Performance and Efficiency
Provisionally accepted
- Quanzhou Normal University, Quanzhou, China
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Rotary Positional Embedding (RoPE) is a widely used technique in Transformers, influenced by the hyperparameter theta (θ). However, the impact of varying *fixed* theta values, especially the trade-off between performance and efficiency on tasks like character-level modeling, remains under-explored. This paper presents a systematic evaluation of RoPE with fixed theta values (ranging from 500 to 50 000) on a character-level GPT model across three datasets: Tiny Shakespeare, Enwik8, and Text8, compared against the standard θ = 10 000 baseline. We find dataset-specific optimal theta values: θ = 5000 for Shakespeare and Text8, and θ = 50 000 for Enwik8, with performance improvements ranging from 0.5% to 2.1%. However, all non-default theta configurations incur significant computational overhead: inference speed is approximately halved across all datasets, suggesting implementation-specific bottlenecks rather than theta-dependent costs. This study quantifies a critical performance-efficiency trade-off when tuning fixed RoPE theta. Our findings emphasize the practical need to balance generalization gains with computational budgets during model development and deployment, contributing empirical insights into RoPE hyperparameter sensitivity and demonstrating that optimal theta selection is highly dataset-dependent. These insights suggest that future positional encoding designs could benefit from adaptive θ scheduling or dataset-specific θ optimization strategies to maximize both performance and computational efficiency.
Keywords: transformer, Positional encoding, Rotary Positional Embedding (RoPE), language modeling, Character-level models, hyperparameter tuning, Computational efficiency
Received: 12 May 2025; Accepted: 28 Jul 2025.
Copyright: © 2025 Huang, Chen and Zheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Musheng Chen, Quanzhou Normal University, Quanzhou, China
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