A 4B model’s BFCL score jumped from 39.51% to 54.18% after one change before supervised fine-tuning and RL: it learned about tools during mid-training. MidTool paper from @Snowflake tests whether tool use should be shaped earlier in a model’s development. Its 20.3-billion-token corpus combines technical webpages, PDFs and code with two forms of synthetic data. - Context-grounded examples turn real documentation, APIs and MCP skills into questions and workflows. - Native agent trajectories teach the model to execute several steps and recover when information is missing. Researchers tested Qwen3-4B-Base and 8B-Base while keeping the downstream recipe fixed: the same SFT data and optional RL. Results: - For the 4B model, MidTool raised BFCL from 39.73% to 50.25% after SFT. After RL: 39.51% → 54.18% - On τ²-Bench, Pass@1 went from 13.04% to 19.96% Another important insight is that executable trajectories helped function calling most, while documentation-grounded data transferred better to unfamiliar environments. Only the complete mixture improved all eight reported metrics. But there’s a boundary: every model scored 0% on MCP-Universe’s web-search subset. Learning tools and workflows wasn’t enough for long-horizon research. So the main lesson from this study: dedicated tool-use mid-training provides a stronger starting point to SFT/RL