Data selection for supervised fine-tuning (SFT) often discards low-scoring instruction examples that may retain useful supervision.
We propose a rule-guided LLM reconstruction framework to reclaim this information.
The framework identifies low-scoring samples through LLM-based rating and score correction, selects semantically representative sources, and reconstructs instruction-response pairs through strategy-guided generation, criteria-based validation, and failure-guided repair.
From 123,786 low-scoring samples, it constructs 54,888 training examples.
Across three 7B/8B base LLMs, the reconstructed data achieves higher average downstream performance than the low-scoring source set and all evaluated data-selection baselines.
Despite using substantially fewer examples than the full source dataset (approximately 300K), it achieves higher reported average scores than full-data training across all three base models.
Stepwise ablations identify rule-guided reconstruction as the largest source of observed improvements over direct fusion, while a manual audit reveals remaining limitations in source integration and answer faithfulness.
These findings support reconstruction as a complementary approach to selection-based SFT data curation.