6 papers
DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning Collections
Haebin Shin, Lei Ji, Xiao Liu +4
As numerous instruction-tuning datasets continue to emerge, dynamically balancing and optimizing their mixtures has become a critical challenge. To address this, we propose Dynamix…
Beyond Sunk Costs: Boosting LLM Pre-training Efficiency via Orthogonal Growth of Mixture-of-Experts
Ruizhe Wang, Yucheng Ding, Xiao Liu +5
As the computational demands for pre-training Large Language Models (LLMs) continue to surge, the need for efficient training paradigms becomes critical. Despite the vast resources…
Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training
Kailai Yang, Xiao Liu, Lei Ji +6
Continual pre-training on small-scale task-specific data is an effective method for improving large language models in new target fields, yet it risks catastrophic forgetting of th…
EpiCoder: Encompassing Diversity and Complexity in Code Generation
Yaoxiang Wang, Haoling Li, Xin Zhang +10
Existing methods for code generation use code snippets as seed data, restricting the complexity and diversity of the synthesized data. In this paper, we introduce a novel feature t…
Velocitune: A Velocity-based Dynamic Domain Reweighting Method for Continual Pre-training
Zheheng Luo, Xin Zhang, Xiao Liu +4
It is well-known that a diverse corpus is critical for training large language models, which are typically constructed from a mixture of various domains. In general, previous effor…
Enhancing Large Language Model Performance with Gradient-Based Parameter Selection
Haoling Li, Xin Zhang, Xiao Liu +4
Large language models (LLMs) have revolutionized lots of fields of research. Although it is well-known that fine-tuning is essential for enhancing the capabilities of LLMs, existin…