Recent advancements in large language models (LLMs) with extended context windows have significantly improved various tasks. To improve long-context capabilities, much work focuses on augmenting LLM’s capabilities with synthetic data. Existing methods often leverage the Self-Instruct framework to generate long-context instruction-tuning data. However, our preliminary experiments show that fewer than 35% of samples generated by Qwen-2-72B are multi-hop, and over 40% exhibit poor quality, limiting comprehensive understanding and further research. To address this, we propose the Multi-agent Interactive Multi-hop Generation (MIMG) framework, which integrates a quality verification agent, a single-hop question generation agent, a multiple question sampling strategy, and a multi-hop question merger agent. This framework significantly improves data quality, with high-quality, multi-hop, and diverse data. Furthermore, we conduct a thorough analysis of document selection, question merging, and validation techniques through extensive experiments across various models. Our results demonstrate that synthetic high-quality long-context instruction data can enhance model performance, surpassing even models trained on larger amounts of human-annotated data.

Large language models have achieved remarkable success, but their extensive parameter size necessitates substantial memory for training, thereby setting a high threshold. While the recently proposed low-memory optimization (LOMO) reduces memory footprint, its optimization technique, akin to stochastic gradient descent, is sensitive to hyper-parameters and exhibits suboptimal convergence, failing to match the performance of the prevailing optimizer for large language models, AdamW. Through analysis of the Adam optimizer, we found that, compared to momentum, the adaptive learning rate is more critical for bridging the gap. Building on this insight, we introduce the low-memory optimization with adaptive learning rate (AdaLomo), which offers an adaptive learning rate for each parameter and exhibits superior convergence performance compared to LOMO theoretically. To maintain memory efficiency, we employ non-negative matrix factorization for the second-order moment estimation. Additionally, we suggest the use of a grouped update normalization to stabilize convergence. Our experiments with instruction-tuning and further pre-training demonstrate that AdaLomo achieves results on par with AdamW, while significantly reducing memory requirements, thereby lowering the hardware barrier to training large language models. The code is accessible at https://github.com/OpenLMLab/LOMO.