大模型指令微调的数据压缩：基于推理贡献度的精化

The fine-tuning of large model instructions based on reasoning data significantly improves the reasoning accuracy of the model by explicitly modeling the multi-step logical correlations of complex tasks. However, the fine-tuning process relies on massive high-quality data, resulting in a sharp increase in computing power overhead. The existing data compression techniques mainly focus on the reduction of the original scale. Generally, there is a lack of compression method design for reasoning data, ignoring multi-step logical associations, semantic dependency relationships in reasoning data, resulting in the damage of the integrity of the key reasoning chain and thereby reducing the reasoning performance. To this end, refinement based on inference contribution (RBIC) is proposed. The knowledge domain graph is constructed by analyzing and inferring the semantic similarity of the data to accurately locate the core information. It combines the semantics of data samples with the reasoning accuracy of large models, divides the difficulty gradient, and covers the reasoning requirements of all scenarios. The reasoning contribution is quantified through the logical complexity of multi-step reasoning data，and the data samples that contribute the most to the model's reasoning are refined. Experimental results show that after fine-tuning based on the reasoning data refined by RBIC, the average reasoning performance of the model only decreases by 1.13%, while the training time is shortened to 16% of the original time consumption. This verifies that RBIC achieves the optimal balance between model efficiency and resource consumption, and is expected to promote the efficient deployment and finetuning optimization of multi-domain large models in resource-constrained environments.