The current codebase lacks comprehensive documentation covering mathematical foundations, parameter semantics, and usage examples for advanced optimizers like GSAM gradient decomposition and ZeroFlow zeroth-order estimation. Future planning should address: first, adding docstrings to all public methods explaining algorithmic rationale and parameter contracts; second, creating usage tutorials demonstrating integration with common deep learning frameworks; third, establishing API stability guarantees with versioned releases; fourth, building benchmark results comparing optimizer performance across different task types; fifth, implementing deprecation cycles for any planned breaking changes. This roadmap will improve accessibility for new users and support long-term maintenance of the project.
The current codebase lacks comprehensive documentation covering mathematical foundations, parameter semantics, and usage examples for advanced optimizers like GSAM gradient decomposition and ZeroFlow zeroth-order estimation. Future planning should address: first, adding docstrings to all public methods explaining algorithmic rationale and parameter contracts; second, creating usage tutorials demonstrating integration with common deep learning frameworks; third, establishing API stability guarantees with versioned releases; fourth, building benchmark results comparing optimizer performance across different task types; fifth, implementing deprecation cycles for any planned breaking changes. This roadmap will improve accessibility for new users and support long-term maintenance of the project.