MindForge: Teaching Small Language Models Whole-Life-Cycle Software Engineering via Source-Free Program Synthesis
MindForge trains a 27B coding model on source-free program synthesis, raising ProgramBench pass rate from 37.98% to 49.51% and improving across seven unseen benchmarks.
MindForge turns open-source CLI programs into source-free training environments exposing only a compiled reference executable and documentation. Trajectories generated with **GLM-5.2** were used to fine-tune **Qwen3.6-27B**, lifting its ProgramBench average test pass rate from **37.98% to 49.51%**.
Builders should distinguish repository editing from whole-program creation when evaluating coding agents. Training against executable behavior and documentation may offer a scalable route to agents that can generate repositories, translate code, fix bugs, and implement features without seeing reference source.
MindForge turns open-source CLI programs into source-free training environments exposing only a compiled reference executable and documentation. Trajectories generated with **GLM-5.2** were used to fine-tune **Qwen3.6-27B**, lifting its ProgramBench average test pass rate from **37.98% to 49.51%**. Builders should distinguish repository editing from whole-program creation when evaluating coding agents. Training against executable behavior and documentation may offer a scalable route to agents that can generate repositories, translate code, fix bugs, and implement features without seeing reference source. The results come from the authors’ benchmark suite rather than production deployments. Frontier models still fully resolve **fewer than 1%** of ProgramBench tasks, and the material does not establish how the pipeline handles graphical apps, services, or undocumented behavior.