Provenance-Grounded Gating and Adaptive Recovery in Synthetic Post-Training Data Curation

The study presents a controlled evaluation of provenance-grounded gating and adaptive recovery in synthetic post-training data curation, demonstrating that grounding filtering signals in source evidence enhances the performance of reward models. Key findings indicate that integrating failure diagnosis with targeted regeneration in an adaptive recovery pipeline significantly improves yield and recovery rates compared to naive resampling. This research is critical for practitioners as it highlights the importance of source provenance in filtering and offers a systematic approach to recover rejected samples, potentially enhancing the quality of fine-tuning data for LLMs.