Course Roadmap Frontier LLM Research

This is a self-study course for learning enough about frontier LLM architectures to participate in serious research conversations and eventually contribute at a frontier lab.

Prerequisites

You do not need to start as a wizard. You do need to become fluent in these:

• Linear algebra: matrix multiplication, eigenvectors, norms, low-rank approximation.
• Probability: conditional probability, entropy, cross-entropy, KL divergence, sampling.
• Optimization: gradient descent, Adam/AdamW, learning rate schedules, loss landscapes.
• Deep learning basics: embeddings, MLPs, normalization, residual connections, backpropagation.
• Systems basics: GPU memory, bandwidth, parallelism, latency, throughput.
• Python/PyTorch: implement attention, train toy models, read profiler output.

Deep prerequisite map: Math and ML Foundations for Frontier LLMs.

Module 1: The Transformer Core

Read:

• Transformer Block Anatomy
• Transformer Math and Implementation Deep Dive
• Attention Mechanics and KV Cache
• Data Tokenization and Pretraining Objective

Learn to answer:

• What exactly is a token representation?
• Why does self-attention mix information differently from an MLP?
• Why do residual streams matter?
• What is the KV cache and why does inference become memory-bound?
• What does next-token prediction actually optimize?

Project:

• Implement a tiny decoder-only transformer in PyTorch.
• Train it on a small text dataset.
• Add causal masking, RoPE or learned positions, KV cache generation, and top-p sampling.
• Write down every tensor shape in the forward pass.

Module 2: Scale And Training Economics

Read:

• Scaling Laws and Compute Optimal Training
• Training Optimization and Stability Deep Dive
• Paper Reading Ladder Frontier LLMs

Learn to answer:

• What does a scaling law predict?
• Why was Chinchilla a major correction to the parameter-count race?
• How do parameter count, token count, data quality, and compute trade off?
• Why is "bigger" not the same as "better trained"?

Project:

• Train a family of small models across parameter and token budgets.
• Plot validation loss against compute.
• Fit simple power-law curves and identify where the experiment becomes noisy.
• Track optimizer, batch size, gradient norm, learning rate, tokens/sec, and validation loss by domain.

Module 3: Modern Architecture Pressure Points

Read:

• Mixture of Experts Architectures
• Long Context and Efficient Sequence Models
• Frontier Model Systems and Inference
• Architecture Concept Graph Frontier LLMs

Learn to answer:

• Why use sparse experts?
• What is expert routing and load balancing?
• Why is attention quadratic in context length?
• Why can long-context benchmarks be misleading?
• Which bottleneck is compute-bound, memory-bound, or communication-bound?

Project:

• Implement top-k MoE routing in a small transformer MLP.
• Implement attention timing comparisons across sequence lengths.
• Measure memory usage with and without KV cache.

Module 4: Post-Training, Alignment, And Reasoning

Read:

• Post Training Alignment and Reasoning
• Evaluation Benchmarks and Scientific Method
• Open Research Questions Frontier LLM Architectures

Learn to answer:

• What changes during instruction tuning?
• Why does RLHF help behavior without changing the pretraining objective?
• How does DPO differ from reward-model RLHF?
• Why are reasoning models often trained with process or outcome signals?
• What is the difference between capability, elicitation, and alignment?

Project:

• Fine-tune a small model with supervised instruction data.
• Train a toy preference model or run DPO on preference pairs.
• Compare base, SFT, and preference-tuned outputs.

Module 5: Researcher Formation

Read:

• Researcher Skill Stack for Frontier Labs
• Open Research Questions Frontier LLM Architectures
• Implementation Roadmap to Frontier Lab Readiness
• Research Memo Template for LLM Papers

Practice:

• Summarize one paper per week in a fixed template.
• Reproduce one small result per month.
• Write one "what would falsify this?" section for every paper.
• Keep a running list of unclear claims, missing baselines, and hidden systems assumptions.

Module 6: Case Studies

Read:

• Frontier Model Case Studies

Learn to answer:

• What did GPT-3 teach about scale?
• What did Chinchilla teach about compute allocation?
• What did LLaMA teach about efficient open recipes?
• What do Mixtral and DeepSeek-V3 teach about sparse MoE tradeoffs?
• What does DeepSeek-R1 teach about reasoning post-training?
• What do restricted reports like GPT-4 teach about reading around missing details?

Project:

• Pick two model reports and compare their implied bottlenecks using Research Memo Template for LLM Papers.

What Lab-Ready Looks Like

You are becoming ready when you can:

• Read architecture papers without getting lost in notation.
• Implement simplified versions of the mechanism.
• Predict bottlenecks before running code.
• Explain why a trick helps at one scale and fails at another.
• Distinguish benchmark improvement from real capability improvement.
• Design experiments that isolate a mechanism rather than just chase leaderboard numbers.
• Explain how data, architecture, optimization, systems, post-training, and eval interact in the result.