Data Compadres

Modern data science workflows are fragmented and slow: answering a single business question often requires multiple specialists (data engineers, analysts, scientists) and weeks of manual work cleaning, transforming, and analyzing data. Current LLM benchmarks focus on text generation, but they rarely test whether models can execute the structured, multi-step workflows required for real data work. DataSage addresses this gap by introducing reinforcement learning environments that simulate the full enterprise data pipeline. Agents must sequentially clean messy datasets, enrich them with domain signals, and generate persona-aware analytical insights. Using GRPO to train LoRA adapters on a 3B model, DataSage learns to enforce data quality, use tools, and reason over structured pipelines—achieving stronger end-to-end performance than GPT-4o-mini on these tasks.