LaMPE: Length-aware Multi-grained Positional Encoding for Adaptive Long-context Scaling Without Training

• URL: http://arxiv.org/abs/2508.02308v2
• Date: Tue, 05 Aug 2025 02:16:08 GMT
• Title: LaMPE: Length-aware Multi-grained Positional Encoding for Adaptive Long-context Scaling Without Training
• Authors: Sikui Zhang, Guangze Gao, Ziyun Gan, Chunfeng Yuan, Zefeng Lin, Houwen Peng, Bing Li, Weiming Hu,
• Abstract summary: Large language models (LLMs) experience significant performance degradation when the input exceeds the pretraining context window.<n>Recent studies mitigate this problem by remapping OOD positions into the in-distribution range with fixed mapping strategies.<n>We propose Length-aware Multi-grained Positional Scaling (LaMPE), a training-free method that fully utilizes the model's effective context window.
• Score: 45.74983991122073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) experience significant performance degradation when the input exceeds the pretraining context window, primarily due to the out-of-distribution (OOD) behavior of Rotary Position Embedding (RoPE). Recent studies mitigate this problem by remapping OOD positions into the in-distribution range with fixed mapping strategies, ignoring the dynamic relationship between input length and the model's effective context window. To this end, we propose Length-aware Multi-grained Positional Encoding (LaMPE), a training-free method that fully utilizes the model's effective context window for adaptive long-context scaling in LLMs. Motivated by the left-skewed frequency distribution of relative positions, LaMPE establishes a dynamic relationship between mapping length and input length through a parametric scaled sigmoid function to adaptively allocate positional capacity across varying input lengths. Meanwhile, LaMPE devises a novel multi-grained attention mechanism that strategically allocates positional resolution across different sequence regions to capture both fine-grained locality and long-range dependencies. Our method can be seamlessly applied to a wide range of RoPE-based LLMs without training. Extensive experiments on three representative LLMs across five mainstream long-context benchmarks demonstrate that LaMPE achieves significant performance improvements compared to existing length extrapolation methods. The code will be released at https://github.com/scar-on/LaMPE.

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