SkipAlign is a novel method designed to enhance the long-context capabilities of large language models (LLMs) without requiring additional data or architectural modifications. It achieves this by strategically modifying positional indices in instruction-following samples to simulate long-range dependencies. SkipAlign leverages the semantic structure of data to expand the context, enabling models to process long instructions more effectively. Through extensive experiments, SkipAlign demonstrates superior performance on long-context tasks, particularly when using a 6B parameter model, achieving results comparable to strong baselines like GPT-3.5-Turbo-16K on LongBench. The method is effective across various context window sizes and improves the model's ability to handle long contexts without extensive data generation. SkipAlign also enhances computational efficiency by avoiding the need for additional long data or architectural changes. The technique involves inserting skipped positions within positional indices to create long-range dependency relations, which are crucial for effective long-context alignment. SkipAlign outperforms conventional instruction fine-tuning and recent packing-based methods, showing significant improvements in both long and short text tasks. The method is evaluated on multiple datasets and demonstrates the importance of long-range dependencies over mere sequence length extension. SkipAlign's effectiveness is further validated through ablation studies and hyperparameter analysis, highlighting its ability to optimize long-context performance while maintaining short-text capabilities. The results indicate that SkipAlign is a promising approach for enhancing LLMs' long-context capabilities with minimal computational overhead.SkipAlign is a novel method designed to enhance the long-context capabilities of large language models (LLMs) without requiring additional data or architectural modifications. It achieves this by strategically modifying positional indices in instruction-following samples to simulate long-range dependencies. SkipAlign leverages the semantic structure of data to expand the context, enabling models to process long instructions more effectively. Through extensive experiments, SkipAlign demonstrates superior performance on long-context tasks, particularly when using a 6B parameter model, achieving results comparable to strong baselines like GPT-3.5-Turbo-16K on LongBench. The method is effective across various context window sizes and improves the model's ability to handle long contexts without extensive data generation. SkipAlign also enhances computational efficiency by avoiding the need for additional long data or architectural changes. The technique involves inserting skipped positions within positional indices to create long-range dependency relations, which are crucial for effective long-context alignment. SkipAlign outperforms conventional instruction fine-tuning and recent packing-based methods, showing significant improvements in both long and short text tasks. The method is evaluated on multiple datasets and demonstrates the importance of long-range dependencies over mere sequence length extension. SkipAlign's effectiveness is further validated through ablation studies and hyperparameter analysis, highlighting its ability to optimize long-context performance while maintaining short-text capabilities. The results indicate that SkipAlign is a promising approach for enhancing LLMs' long-context capabilities with minimal computational overhead.