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[2602.10431] QTALE: Quantization-Robust Token-Adaptive Layer Execution for LLMs
Summary
The paper presents QTALE, a framework that integrates token-adaptive layer execution with quantization for large language models, improving efficiency without sacrificing accuracy.
Why It Matters
As large language models become more prevalent, efficient deployment is crucial. QTALE addresses the dual challenges of computational resource demands and memory constraints, making it significant for developers and researchers focused on optimizing AI performance.
Key Takeaways
- QTALE combines token-adaptive execution with quantization to enhance LLM efficiency.
- The framework maintains accuracy, with less than 0.5% difference compared to quantization-only models.
- Key innovations include a diverse training strategy and a flexible post-training execution adjustment.
Computer Science > Machine Learning arXiv:2602.10431 (cs) [Submitted on 11 Feb 2026 (v1), last revised 25 Feb 2026 (this version, v3)] Title:QTALE: Quantization-Robust Token-Adaptive Layer Execution for LLMs Authors:Kanghyun Noh, Jinheon Choi, Yulhwa Kim View a PDF of the paper titled QTALE: Quantization-Robust Token-Adaptive Layer Execution for LLMs, by Kanghyun Noh and 2 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) demand substantial computational and memory resources, posing challenges for efficient deployment. Two complementary approaches have emerged to address these issues: token-adaptive layer execution, which reduces floating-point operations (FLOPs) by selectively bypassing layers, and quantization, which lowers memory footprint by reducing weight precision. However, naively integrating these techniques leads to additional accuracy degradation due to reduced redundancy in token-adaptive models. We propose QTALE (Quantization-Robust Token-Adaptive Layer Execution for LLMs), a novel framework that enables seamless integration of token-adaptive execution with quantization while preserving accuracy. Conventional token-adaptive methods reduce redundancy in two ways: (1) by limiting the diversity of training paths explored during fine-tuning, and (2) by lowering the number of parameters actively involved in inference. To overcome these limitations, QTALE introduces two key components: (1) a training strategy that ensures diverse execu...
