Build a Reasoning Model (From Scratch)

Teaches the foundational architectural patterns for building reasoning models from first principles, covering the core components like input processing, intermediate reasoning steps, and output generation. Uses a pedagogical approach that breaks down complex reasoning systems into modular, understandable components with clear data flow between stages.

Covers the methodology for curating, structuring, and preparing training datasets specifically designed to teach models multi-step reasoning capabilities. Includes techniques for generating synthetic reasoning chains, annotating intermediate steps, and balancing dataset composition to encourage generalizable reasoning patterns rather than memorization.

Explains how to design and implement loss functions that optimize for correct intermediate reasoning steps, not just final answers. Covers techniques like step-level supervision, reasoning path ranking, and auxiliary losses that encourage the model to develop interpretable reasoning chains while maintaining end-task performance.

Teaches techniques for generating reasoning chains during inference, including beam search over reasoning paths, self-consistency verification across multiple chains, and validation mechanisms to ensure reasoning steps are logically coherent. Covers both greedy decoding and sampling strategies optimized for reasoning quality.

Defines and implements metrics for assessing reasoning model performance beyond final answer accuracy, including intermediate step correctness, reasoning path diversity, explanation quality, and logical consistency. Covers both automatic metrics and human evaluation protocols for comprehensive reasoning assessment.

Addresses architectural and training techniques for building reasoning models that can handle longer reasoning chains without degradation. Covers attention mechanisms for long-range dependencies, memory-augmented architectures, and training strategies that prevent error accumulation across many reasoning steps.

Provides frameworks for adapting reasoning model architectures and training procedures to specific domains (mathematics, code, scientific reasoning, etc.). Includes domain-specific loss functions, specialized tokenization, and task-adapted reasoning patterns that improve performance on domain problems.

Covers techniques for making reasoning model internals interpretable, including attention visualization, reasoning step explanation generation, and methods for understanding what reasoning patterns the model has learned. Enables inspection of intermediate representations and verification that reasoning is actually occurring.

Teaches how to augment reasoning models with external tools (calculators, search engines, knowledge bases) that the model can invoke during reasoning. Covers tool-use protocols, integration patterns, and training techniques that teach models when and how to use external resources to improve reasoning.

Provides systematic approaches for identifying failure modes in reasoning models, including techniques for analyzing where reasoning chains break down, categorizing error types, and diagnosing whether failures are due to reasoning errors or knowledge gaps. Includes debugging workflows and tools for root cause analysis.