reasoning from scratch

The implementation walks through each component that enables extended multi-step reasoning: the base transformer architecture, supervised fine-tuning on chain-of-thought formatted data, reward model training to evaluate reasoning quality, and reinforcement learning procedures (GRPO, RLVR) that teach the model to allocate more thinking steps to harder problems.

Unlike purely theoretical treatments, the code is written to be runnable on accessible hardware, with design choices annotated to explain how they relate to published reasoning model research.

The resource is particularly valuable for ML engineers who understand standard LLM training pipelines and want to develop practical knowledge of reasoning model training a domain that has become commercially significant following the demonstrated performance improvements reasoning models achieve on mathematical, scientific, and coding benchmarks.

By building each component from scratch rather than using opaque library abstractions, the curriculum builds genuine understanding of how test-time compute scaling produces qualitatively different model capabilities.