This work explores efficient ultra-long context modeling.We posit that an effective solution requires three fundamental properties: sparsity, random-access flexibility, and length generalization.To achieve this, we leverage Hierarchical Sparse Attention (HSA), a novel attention mechanism that satisfies all three properties.We integrate HSA into the Transformer architecture to develop HSA-UltraLong, an 8B-parameter Mixture-of-Experts (MoE) model trained on over 8 trillion tokens.We rigorously evaluate the model across tasks with both in-domain and out-of-domain context lengths to validate its capabilities.Our model demonstrates comparable performance to full-attention baselines on in-domain sequence lengths.Crucially, it achieves over 90% accuracy on most in-context retrieval tasks with contexts up to 512 times the pre-training context length.This work reports our findings and remaining issues throughout the experiments, offering insights for future research in ultralong context modeling.

While Transformer models have achieved remarkable success in various domains, the effectiveness of information propagation through deep networks remains a critical challenge.Standard hidden state residuals often fail to adequately preserve initial token-level information in deeper layers.This paper introduces ResFormer, a novel architecture that enhances information flow by incorporating value residual connections in addition to hidden state residuals.And a variant is SVFormer, where all layers share the first layer's value embedding.Comprehensive empirical evidence demonstrates ResFormer achieves equivalent validation loss with 16.11% fewer model parameters and 20.3% less training data compared to Transformer, while maintaining similar memory usage and computational cost.Besides, SVFormer reduces KV cache size by nearly half with only a small performance penalty and can be integrated with other KVefficient methods, yielding further reductions in KV cache, with performance influenced by sequence length and cumulative learning rate.

Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models (LLMs). While numerous studies have examined the impact of factors such as data volume and model size on English models, the scaling properties of instruction tuning in other languages remain largely unexplored. In this work, we systematically investigate the effects of data quantity, model size, and data construction methods on instruction tuning for Chinese LLMs. We utilize a newly curated dataset, DoIT, which includes over 40,000 high-quality instruction instances covering ten underlying abilities, such as creative writing, code generation, and logical reasoning. Our experiments, conducted on models ranging from 7b to 33b parameters, yield three key findings: (i) While these factors directly affect overall model performance, some abilities are more responsive to scaling, whereas others demonstrate significant resistance. (ii) The scaling sensitivity of different abilities to these factors can be explained by two features: Complexity and Transference. (iii) By tailoring training strategies to their varying sensitivities, specific abilities can be efficiently learned, enhancing performance on two public benchmarks.