Concise monthly summary for 2026-01 focusing on NVIDIA/NeMo-Skills. Key achievement: delivered new evaluation assets for mathematical problem solving to strengthen LLM benchmarking and model selection. No major bugs fixed this month in this repo. Overall impact: enhanced benchmarking rigor and reproducibility, enabling faster iteration and more reliable math-task evaluations. Technologies/skills demonstrated: dataset design and curation, evaluation metric development, version-controlled release and collaboration (Git), and reproducible research practices.

December 2025 (Month: 2025-12) focused on delivering foundational improvements to Lean 4 theorem proving within NVIDIA/NeMo-Skills, expanding autoformalization capabilities, and enhancing data documentation and message formatting for GPT-OSS outputs. The work emphasizes business value through more reliable formal proofs, better reproducibility, and streamlined developer workflows. Key outcomes include refactoring proof utilities, introducing autoformalization for generating and refining formal proofs from natural language with backtranslation and error refinement, comprehensive dataset documentation and clarifications on self-correction, and improved assistant message parsing to align with chat templates.

October 2025 monthly summary for NVIDIA/NeMo-Skills: delivered a feature enhancement to the Putnam benchmark script to improve lean file parsing and dataset processing, via commit b30c99531730656e94bae48c7610bb16810be9d9 (PR #1013). This month had no major bugs fixed; focus was on delivering a reliable and maintainable benchmark prep workflow.

September 2025 performance highlights for Kipok/NeMo-Skills: delivered key features including a new benchmark and dataset upgrades, fixed critical robustness issues, and reinforced data quality and reproducibility to accelerate experimentation and decision-making.