According to God of Prompt on Twitter, citing a Google AI experiment, standard fine-tuning of Gemini 2.5 Flash on a text-to-SQL dataset reduced performance on the hardest queries, indicating reasoning degradation without explicit reasoning traces. As reported by the tweet, the base Gemini 2.5 Flash scored 73.17% overall vs 72.50% after fine-tuning, but on the hardest 40 queries it fell from 62.5% to 57.5%, a failure mode Google calls representation collapse. According to the same source, a Qwen 7B model improved from 36.17% baseline to 45.33% with standard fine-tuning, and to 54.5% when trained with Chain of Thought steps, nearly halving the gap with Gemini 2.5 Flash. The business takeaway, according to the thread, is that large models risk losing multi-step reasoning when fine-tuned on plain IO pairs, while small models gain materially when trained on structured reasoning traces, making CoT-style fine-tuning and data format design a high-ROI strategy for enterprise text-to-SQL and analytics automation.