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Build Your Own Reasoning Model

The Reasoning Sovereignty Stack™ — Stop Renting Reasoning, Start Owning It

The ONLY masterclass teaching you to build o1-class reasoning systems from scratch—own your reasoning technology, stop renting from OpenAI.

This is not another course on using reasoning APIs. This is executive business education (Harvard/MIT/Stanford caliber) merged with a masterclass for tech founders and AI leaders. Using the DrLee.AI Shu-Ha-Ri learning method, you'll go from reasoning API consumer to reasoning architect in 9 transformative steps.

Each module begins with a TedTalk-style presentation, then you immediately build it yourself with hands-on coding. You'll implement process-supervised reward modeling, train reasoning with reinforcement learning, deploy inference-time compute scaling, and build the breakthrough techniques behind o1, o3, and DeepSeek-R1.

Different from our LLM course: While "Build Your Own LLM" teaches you to construct the base transformer architecture, this course focuses on adding advanced reasoning capabilities to existing models—the secret sauce that makes o1 and o3 dramatically better at math, code, and complex problem-solving than standard models.

By the end, you won't just understand how reasoning models work—you'll own production-ready reasoning systems that become your competitive moat.

FROM
API Consumer
$100K-$150K · Replaceable Skills
TO
Model Builder
$250K-$400K · Irreplaceable
9 weeks · 50 hours · Own your model weights forever
Start Your TransformationSee The Journey
The Reasoning Sovereignty Stack™

Your 9-Step Transformation Journey

Each step follows the Shu-Ha-Ri method: TedTalk inspiration → Hands-on coding → Experimentation → Innovation.Watch as you progress from API consumer to model builder, building your competitive moat with every step.

Weeks 1-3

Foundation

Understanding Core Reasoning Principles

FROM
“What makes o1 different? How does reasoning work? Can't explain PSRM or inference scaling.”
TO
“I can explain reasoning architecture, process-supervised reward modeling, and evaluation methods. I understand how o1, o3, and DeepSeek-R1 work internally.”
🛡️ Knowledge Foundation
Speak the language of frontier reasoning AI—communicate with researchers, engineers, and executives about reasoning capabilities. Understand what 99% of 'AI engineers' don't.
Weeks 4-6

Implementation

Building Production Reasoning Systems

FROM
“Never trained a reasoning model. Don't know how to implement PSRM or RL training. Can't build inference scaling systems.”
TO
“I implemented inference-time scaling, trained process reward models, applied RL for reasoning, and distilled into efficient models. I've built working reasoning systems.”
🛡️ Implementation Mastery
No more black boxes in reasoning—you can code PSRM, inference scaling, and RL training from scratch. Modify and optimize reasoning systems at will.
Weeks 7-9

Mastery

Leading Frontier Reasoning Initiatives

FROM
“Only know basic reasoning. Can't build tool-augmented systems. Don't know production optimization or frontier techniques.”
TO
“I build tool-augmented reasoning, deploy at production scale, and implement cutting-edge techniques like o3's search. I lead reasoning AI initiatives.”
🛡️ Production Excellence + Frontier Positioning
Deploy reasoning at scale with production infrastructure, implement frontier techniques before they become mainstream, and maintain edge as field evolves.

The Complete Transformation Matrix

Each step follows the Shu-Ha-Ri cycle: TedTalk inspiration → Hands-on coding → Experimentation → Innovation.This is the guided progression that transforms API consumers into model builders.

1

Step 1: The Intelligence Behind Reasoning

FROM (Point A)
“What makes o1 and reasoning models different from standard models?”
TO (Point B)
“I can explain how reasoning models work, why they outperform base models, and the architectural principles that enable step-by-step thinking”
🛡️ Knowledge Moat
Speak the language of frontier reasoning AI
2

Step 2: Text Generation Foundations

FROM (Point A)
“How do I prepare data and generate text for reasoning tasks?”
TO (Point B)
“I can implement sampling strategies, control generation parameters, and prepare datasets for reasoning model training”
🛡️ Data Engineering Moat
Control the generation pipeline for reasoning data
3

Step 3: Measuring Reasoning Quality

FROM (Point A)
“How do you evaluate if a reasoning model is working?”
TO (Point B)
“I can implement judgment-based and benchmark-based evaluation, measure reasoning quality, and identify failure modes”
🛡️ Quality Assurance Moat
Objectively measure reasoning performance
4

Step 4: Scaling Intelligence at Inference Time

FROM (Point A)
“How does o1 use 'thinking time' to get better answers?”
TO (Point B)
“I can implement inference-time compute scaling with search algorithms, beam search, and best-of-N sampling”
🛡️ Inference Mastery Moat
Control the intelligence-compute tradeoff
5

Step 5: Learning to Reason Through Reinforcement

FROM (Point A)
“How do you train models to reason step-by-step?”
TO (Point B)
“I can implement process-supervised reward models, outcome rewards, and RL training pipelines for reasoning”
🛡️ Training Mastery Moat
Train reasoning from scratch—the most valuable skill
6

Step 6: Knowledge Compression and Efficiency

FROM (Point A)
“Can I distill reasoning from o1 into faster models?”
TO (Point B)
“I can implement distillation to transfer reasoning capabilities from large models to smaller, faster ones”
🛡️ Efficiency Moat
Compress reasoning into fast, deployable models
7

Step 7: Advanced Reasoning Architectures

FROM (Point A)
“How can I improve reasoning beyond standard techniques?”
TO (Point B)
“I can implement tool integration, retrieval-augmented reasoning, and multi-model reasoning systems”
🛡️ Architectural Innovation Moat
Design novel reasoning systems
8

Step 8: Production Integration Techniques

FROM (Point A)
“How do I deploy reasoning models in production?”
TO (Point B)
“I can implement efficient serving, caching, batching, and monitoring for production reasoning systems”
🛡️ Production Excellence Moat
Deploy reasoning at scale
9

Step 9: Frontier Reasoning Capabilities

FROM (Point A)
“What's next in reasoning AI?”
TO (Point B)
“I understand cutting-edge techniques like o3-style search, recursive reasoning, and continuous improvement”
🛡️ Frontier Position Moat
Stay at cutting edge of reasoning AI

The Shu-Ha-Ri Learning Method

Ancient Japanese martial arts philosophy adapted for elite technical education. Each module follows this complete cycle—by Step 9, you've experienced Shu-Ha-Ri nine times, building deeper mastery with every iteration.

📚

Shu (守) - Learn

TedTalk-style masterclass + guided hands-on coding

“Watch attention mechanisms explained, then code them yourself with step-by-step guidance”

🔨

Ha (破) - Break

Modify code, experiment with parameters, adapt to your problems

“Change attention heads from 8 to 12, try different learning rates, debug training instability”

🚀

Ri (離) - Transcend

Apply independently, innovate beyond what's taught

“Design novel architectures for your domain, solve your specific business problems, lead AI initiatives”

This is how you transcend from passive learner to active innovator. This is executive business education merged with hands-on mastery.

Proven Transformation Results

Real outcomes from students who completed The Reasoning Sovereignty Stack™ and built their competitive moats

📈 Career Transformation

80%
Promoted to Senior/Staff Reasoning AI Engineer
$150K-$250K
Average salary increase (reasoning premium)
95%
Report being 'irreplaceable' (reasoning expertise rare)
90%
Lead reasoning AI initiatives after completion

💰 Business Impact

$120K-$600K
Annual reasoning API cost elimination
85%
Eliminate reasoning API dependencies entirely
75%
Raise funding with proprietary reasoning moat
2-4 months
Average time to ROI on reasoning investment

What You'll Actually Build

🧠
Process Reward Model
PSRM from scratch
⚡
Inference Scaling
Best-of-N, beam search
🎯
RL Training
GRPO/PPO for reasoning
📦
Distillation
o1 → small model
🔧
Tool-Augmented
Multi-step reasoning

Choose Your Path to Mastery

All modalities include the complete Reasoning Sovereignty Stack™. Choose based on your learning style and goals.

Self-Paced Mastery

$1,997
Lifetime Access
Self-directed learners
  • All 9 modules (40+ hours)
  • Complete PyTorch code repositories
  • Reasoning datasets (math, code, logic)
  • Private community access
  • Code review from TAs (48-hour turnaround)
  • Monthly group office hours
  • Lifetime access to updates
Most Popular

9-Week Live Cohort

$6,997
12 Weeks
Engineers wanting accountability
  • Everything in Self-Paced PLUS:
  • Live weekly workshops (2 hours)
  • Real-time code reviews and feedback
  • Direct instructor access (office hours 2x/week)
  • 1-on-1 kickoff and graduation calls
  • Cohort accountability partners
  • Priority support and 24-hour code review
  • Alumni network access

Founder's Edition

$19,997
6 Months
Founders & technical leaders
  • Everything in Cohort PLUS:
  • 6 months of 1-on-1 coaching (60 min/month)
  • Direct Slack/WhatsApp instructor access
  • Same-day code review turnaround
  • Architecture review for your use case
  • Done-with-you: Build YOUR reasoning model
  • Career/investor pitch coaching
  • Exclusive alumni group and network

5-Day Immersive Bootcamp

Executive format: Monday-Friday intensive (8am-6pm). Build complete GPT in one week. Limited to 15 participants for maximum attention.

Course Curriculum

9 transformative steps · 40 hours of hands-on content

1

Step 1: The Intelligence Behind Reasoning

6 lessons · Shu-Ha-Ri cycle

  • What Makes Reasoning Models Different from Base LLMs
  • The Power of Step-by-Step Thinking in o1 and o3
  • Process vs. Outcome Supervision: The PSRM Breakthrough
  • Inference-Time Compute Scaling: Why o1 Takes Time to Think
  • Real-World Reasoning Applications: Math, Code, Logic, Planning
  • The Reasoning Model Pipeline: From Base LLM to o1-Class System
2

Step 2: Text Generation Foundations

6 lessons · Shu-Ha-Ri cycle

  • Sampling Strategies for Reasoning: Temperature, Top-p, Top-k
  • Temperature and Creativity in Reasoning Chains
  • Nucleus and Top-K Sampling for Quality Control
  • Chain-of-Thought Data Preparation and Formatting
  • Best-of-N Sampling: Exploring Solution Space
  • Generating Diverse Reasoning Chains for Training
3

Step 3: Measuring Reasoning Quality

6 lessons · Shu-Ha-Ri cycle

  • Outcome-Based Evaluation: Automated Grading
  • Process-Based Evaluation: Scoring Reasoning Steps
  • Benchmark Implementation: GSM8K, MATH, HumanEval
  • Failure Mode Analysis: Identifying Reasoning Errors
  • Pass@K Metrics: Measuring with Multiple Attempts
  • Building Automated Evaluation Pipelines
4

Step 4: Scaling Intelligence at Inference Time

6 lessons · Shu-Ha-Ri cycle

  • Best-of-N with Rewards: Quality Scaling Through Compute
  • Beam Search for Reasoning: Maintaining Top-K Paths
  • Adaptive Inference Budgets: Allocating Compute Wisely
  • Tree Search for Reasoning: Exploring Branching Paths
  • Latency-Quality Tradeoffs: Production Optimization
  • Implementing Efficient Inference Scaling
5

Step 5: Learning to Reason Through Reinforcement

6 lessons · Shu-Ha-Ri cycle

  • Process-Supervised Reward Modeling (PSRM): The Core Breakthrough
  • Outcome Reward Models: Rewarding Correct Solutions
  • Collecting Process Annotations: Labeling Reasoning Steps
  • Policy Gradient Training: REINFORCE and PPO for Reasoning
  • Reward Model Accuracy: Measuring and Improving Quality
  • Complete RL Training Loop Implementation
6

Step 6: Knowledge Compression and Efficiency

6 lessons · Shu-Ha-Ri cycle

  • Reasoning Distillation: Transferring from Teacher to Student
  • Chain-of-Thought Distillation: Compressing Reasoning Patterns
  • Implicit Reasoning: Internal Processing Without Visible CoT
  • Outcome Supervision for Efficiency: Simplifying for Speed
  • Distillation Data Quality: Generating High-Quality Examples
  • Deploying Fast Reasoning Models
7

Step 7: Advanced Reasoning Architectures

6 lessons · Shu-Ha-Ri cycle

  • Tool-Augmented Reasoning: Calculators, Code, Search Integration
  • Retrieval-Augmented Reasoning: Grounding in Knowledge Bases
  • Multi-Step Tool Chains: Agentic Reasoning Systems
  • Verification and Self-Correction: Automated Error Detection
  • Ensemble Reasoning: Combining Multiple Strategies
  • Building Novel Reasoning Architectures
8

Step 8: Production Integration Techniques

6 lessons · Shu-Ha-Ri cycle

  • KV Caching for Reasoning: 2-5x Latency Reduction
  • PyTorch Compilation: 10-30% Speedup with torch.compile()
  • Batched Inference: Maximizing GPU Utilization
  • Reasoning Monitoring: Tracking Quality in Production
  • Cost Optimization: Reducing Inference Costs at Scale
  • Building Production Serving Infrastructure
9

Step 9: Frontier Reasoning Capabilities

6 lessons · Shu-Ha-Ri cycle

  • Search-Based Reasoning (o3): Next-Generation Techniques
  • Recursive Reasoning: Meta-Reasoning Systems
  • Continuous Improvement: Production Data Feedback Loops
  • Domain-Specific Reasoning: Specialization Strategies
  • The Reasoning Frontier: Multi-Modal, Long-Horizon Planning
  • Staying Current in Rapidly Evolving Field

Production-Grade Tech Stack

Master the same tools used by OpenAI, Anthropic, and Google to build frontier AI systems

For Career Advancers

I help AI engineers and technical leaders build production-ready reasoning models from scratch—the breakthrough behind o1, o3, and DeepSeek—so they can command $250K-$400K salaries and architect next-generation AI systems without being limited to API wrappers, stuck explaining 'prompt engineering' skills, or missing the reasoning revolution that's creating the next wave of $300K+ AI architect roles.

For Founders & CTOs

I help technical founders and CTOs build proprietary reasoning models that create defensible competitive moats—like o1 but owned and customized—so they can eliminate $100K-$500K in annual API costs and own reasoning capabilities without being held hostage by OpenAI rate limits, building 'thin wrapper' businesses that VCs won't fund, or spending $300K-$500K hiring ML engineers who may not understand reasoning systems.

PyTorchTransformersProcess Reward ModelsGRPOPPOvLLMWeights & Biases

Frequently Asked Questions

Isn't this too advanced for me?

If you understand transformers and basic RL concepts, you're ready. We build from first principles, starting with reward modeling and working up to full reasoning systems. Every concept is explained and coded step-by-step.

Can't I just use the o1 API?

You can—if you're okay with $50K/year in reasoning costs, zero differentiation, vendor lock-in, and being viewed as an API consumer instead of a reasoning architect. APIs are for prototypes. Model ownership is for production and moats.

What hardware do I need?

A laptop with a GPU is sufficient. We use compact base models (7B parameters or smaller) that fit in 8GB VRAM for reasoning training. Cloud GPU options are provided for faster experiments.

How does this differ from the Large Language Models course?

The LLM course teaches you to build the base model architecture. This course assumes you have a pre-trained model and focuses on adding reasoning capabilities through PSRM training, inference scaling, and distillation—the techniques that make o1 and o3 work.

Will I build a working reasoning model?

Yes. By the end, you'll have a production-ready reasoning system with process-supervised reward modeling, achieving competitive performance on math and code benchmarks—built entirely from scratch.

What's the business case for reasoning models?

Reasoning models enable reliable automation of complex knowledge work. For engineers: command $250K-$400K salaries. For founders: eliminate $120K-$600K annual reasoning API costs and create defensible moats through proprietary reasoning.

Is reasoning too complex to build myself?

DeepSeek-R1 proved reasoning models can be built openly. We teach the exact techniques: process rewards, chain-of-thought supervision, inference scaling. If DeepSeek can do it, so can you with proper guidance.

Do I need to take the LLM course first?

Not required, but recommended. This course includes a refresher on transformer architecture, but deeper understanding from the LLM course will accelerate your learning of reasoning concepts.

Stop Renting AI. Start Owning It.

Join 500+ engineers and founders who've gone from API consumers to model builders—building their competitive moats one step at a time.

Command $250K-$400K salaries or save $100K-$500K in annual API costs. Own your model weights. Build defensible technology moats. Become irreplaceable.

Starting at
$1,997

Self-paced · Lifetime access · 30-day guarantee

Start Your Transformation

This is not just education. This is technological sovereignty.

30-day guarantee
Lifetime updates
Zero API costs forever