Final Projects: Build a Reproducible Generative-Model Experiment | CMU 10-423 Topic Guide
Design controlled comparisons, retain paired outcomes, and distinguish replication from working code in a generative-model project.
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47 postsAutodiff in Practice: From Hand Gradients to a Training Loop | CMU 10-423 Assignment 0
Knowing that backpropagation uses the chain rule is different from implementing a correct training loop.
Paper Reports: Turn a Model Summary into a Testable Argument | CMU 10-423 Assignment 623
Separate paper claims, empirical support, and your own interpretation through a role-based report and a controlled-comparison example.
Causal Attention: From Q, K, and V to the Next Token | CMU 10-423 Lecture 2
An RNN compresses the prefix into a state.
Training and Decoding: Why the Same Logits Produce Different Sequences | CMU 10-423 Lecture 3
Learning a next-token distribution and choosing a token from that distribution are consecutive but distinct operations.
Diffusion Training and Sampling: Random Timesteps and Reverse Chains | CMU 10-423 Lecture 8
Lecture 7 constructed noisy inputs and noise targets.
VAEs: Reconstruction, KL, and Reparameterization | CMU 10-423 Lecture 9
A conventional autoencoder compresses an input into a vector and reconstructs it.
ICL, SFT, and RLHF: From Changing Inputs to Changing Weights | CMU 10-423 Lecture 11
Separate what ICL, SFT, reward models, and RLHF each change, with worked examples and answered variations.
DPO and Latent Diffusion: Updating on Preferences, Conditioning the Denoiser | CMU 10-423 Lecture 12
Derive the DPO loss with a recomputable example, then trace shapes and conditioning paths in latent diffusion.
Vision-Language Models: Turning Images into Language-Model Inputs | CMU 10-423 Lecture 13
A complete worked example and answered variations for Vision-Language Models: Turning Images into Language-Model Inputs.
Cross-Attention and DiT: How Conditions Enter a Denoiser | CMU 10-423 Lecture 14
A complete worked example and answered variations for Cross-Attention and DiT: How Conditions Enter a Denoiser.
Q-Former and Scaling Laws: Bridging Frozen Models, Allocating a Fixed Budget | CMU 10-423 Lecture 15
Trace Q-Former queries and two-stage training, then compute a model/data split under a fixed compute budget.
Mixture of Experts: Separating Capacity from Active Computation | CMU 10-423 Lecture 16
A complete worked example and answered variations for Mixture of Experts: Separating Capacity from Active Computation.
Distributed Training: Splitting Data, Models, and Optimizer State | CMU 10-423 Lecture 17
A complete worked example and answered variations for Distributed Training: Splitting Data, Models, and Optimizer State.
Efficient Inference: FlashAttention, KV Caches, and Speculative Decoding | CMU 10-423 Lecture 18
A complete worked example and answered variations for Efficient Inference: FlashAttention, KV Caches, and Speculative Decoding.
Long Context: Fitting Input Is Not Reliable Retrieval | CMU 10-423 Lecture 19
Long Context: Fitting Input Is Not Reliable Retrieval | CMU 10-423 Lecture 19
State Space Models: From Recurrence to Convolution | CMU 10-423 Lecture 21
State Space Models: From Recurrence to Convolution | CMU 10-423 Lecture 21
Generative AI Risks: Turning Failures into Tests | CMU 10-423 Lecture 22
Generative AI Risks: Turning Failures into Tests | CMU 10-423 Lecture 22
Audio Understanding and Generation: Samples to Text, Tokens Back to Sound | CMU 10-423 Lecture 24
Compute frames, mel bins, and WER, then account for codec tokens and contrast diffusion sampling.
Video Generation: Modeling the Time Axis | CMU 10-423 Lecture 25
Video Generation: Modeling the Time Axis | CMU 10-423 Lecture 25
World Models and Alignment Theory: Action Interventions and Corrigibility Assumptions | CMU 10-423 Lecture 26
Separate three world representations and evidence of action control, then examine consensus costs and corrigibility theorem conditions.
LoRA: What Changes When Most Weights Stay Frozen | CMU 10-423 Lecture 10
A complete worked example and answered variations for LoRA: What Changes When Most Weights Stay Frozen.
Practice Exam: Diagnose Conceptual Gaps | CMU 10-423 Topic Guide
Use the public practice exam to diagnose assumptions, data flow, and evaluation errors through a worked probability example.
Audio, Video, and World Models: A Reading Map | CMU 10-423 Topic Guide
Audio representation, temporal relationships in video, and action-conditioned world transitions are different learning tasks. The earlier combined guide checked only audio at the end. This URL now serves as a reading map to one full guide each for Lectures 24–26; the Lecture 26 guide also covers the separate alignment deck.
CNNs and ViT: Turning an Image into Model Inputs | CMU 10-423 Lecture 5
Text models convert text into tokens; image models also need a computational representation of pixels.
Code Generation and Agents: A Reliable Test–Repair Loop | CMU 10-423 Lecture 23
Code Generation and Agents: A Reliable Test–Repair Loop | CMU 10-423 Lecture 23
Condition Injection: The Roles of Attention, DiT, and Q-Former | CMU 10-423 Topic Guide
Cross-attention reads another representation, DiT is a diffusion-backbone choice, and Q-Former uses learned queries to extract or bridge information. They can coexist rather than being three competing products. The Lecture 14 lesson handles Prompt-to-Prompt and classifier-free guidance in detail.
Diffusion Models: Why Adding Noise Creates a Training Task | CMU 10-423 Lecture 7
A GAN learns from a discriminator.
Distributed Training and Efficient Inference: Locate the Cost | CMU 10-423 Topic Guide
A model that does not fit on one GPU has a different problem from a model that fits but decodes slowly. Distributed strategies partition data, parameters, and states. FlashAttention improves attention memory traffic; KV caching avoids recomputing historical keys and values during decoding.
Exams, Papers, and Projects: Three Forms of Learning Evidence | CMU 10-423 Topic Guide
An exam tests reasoning under a fixed question. A paper report tests your judgment of someone else’s evidence. A project tests your ability to produce reproducible evidence. These are distinct learning tasks, now taught separately while this URL remains an assessment map.
GANs: What the Generator and Discriminator Learn | CMU 10-423 Lecture 6
A classifier asks which class an image belongs to.
A Generative AI Learning Path: Reading the 35 Core Guides | CMU 10-423 Course Overview
This series uses the public Spring 2026 materials for CMU 10-423/623/723 Generative AI.
Testing minGPT Changes: Shapes, Masks, and Positions | CMU 10-423 Assignment 1
Assignment 1 brings sequence modeling and modern attention into code.
Testing DDPM: Matching Timestep Coefficients to a Batch | CMU 10-423 Assignment 2
Copying a noising equation is not enough to implement diffusion correctly.
Testing LoRA Fine-Tuning: Gradients, Data, and Evaluation | CMU 10-423 Assignment 3
Use an original numerical test to check gradients, data, and evaluation alongside the original assignment specification.
Testing Q-Former Text-to-Image: Gradients through Frozen Backbones | CMU 10-423 Assignment 4
Use an original numerical test to check gradients, data, and evaluation alongside the original assignment specification.
SFT, RLHF, and DPO: Separate Their Training Signals | CMU 10-423 Topic Guide
A prompt with one demonstrated answer is not the same data as a prompt with a preferred and rejected response. SFT learns demonstrated tokens. A typical RLHF pipeline learns reward and then updates a constrained policy. DPO forms a loss directly from preference pairs and reference-relative ratios.
Long Context and SSMs: Different Ways to Retain History | CMU 10-423 Topic Guide
Long-context evaluation asks whether a model can process, retrieve, and use distant information. An SSM specifies sequence processing through state updates. SSM efficiency does not establish reliable document retrieval, and a large attention window does not guarantee relevant evidence is used.
Modern Transformers: What Position Rotation and KV Sharing Change | CMU 10-423 Lecture 4
Attention needs both order information and a manageable memory footprint during generation.
PEFT and ICL: Change Parameters or Change the Input | CMU 10-423 Topic Guide
Adapting without retraining every weight can mean two different things. LoRA-style PEFT still trains some parameters. ICL supplies examples in context without updating parameters during that inference. The source spans the end of Lecture 9, Lecture 10, and the ICL material in Lecture 11.
Reasoning Models: Spending Compute on Verifiable Answers | CMU 10-423 Lecture 20
Reasoning Models: Spending Compute on Verifiable Answers | CMU 10-423 Lecture 20
Operational Risks and Alignment Theory: Tests and Assumptions | CMU 10-423 Topic Guide
Hallucination, leakage, and prompt injection are observable operational risks. Alignment theory asks which guarantees hold under specific formal assumptions. They require different evidence. Lectures 22 and 26 therefore have separate teaching guides, without turning one theorem into a universal impossibility claim.
RNN Language Models: Sequence Probabilities and Gradient Paths | CMU 10-423 Lecture 1
A sequence such as “I like cats” is not a bag of independent words.
Scaling and MoE: Compute Budgets and Conditional Computation | CMU 10-423 Topic Guide
Scaling laws study model and data allocation under a budget. MoE increases expert capacity while activating selected experts. The former concerns allocation and empirical models; the latter concerns architecture and routing. Both deserve separate checks.
Text-to-Image and VLMs: Generating Images versus Reading Them | CMU 10-423 Topic Guide
“Text plus images” does not specify the task. Text-to-image generation produces an image conditioned on text; a VLM may consume an image and produce an answer. Direction, output objective, and evaluation differ even when both use Transformers.
Transformer, Training, and Decoding: Three Different Questions | CMU 10-423 Topic Guide
How the model reads a history, learns from targets, and chooses its next token are different questions. Attention mixes information; the training objective updates parameters; decoding selects output under a fixed model. Keeping them distinct prevents confusing a sampling change with a model update.
Diffusion and VAEs: Related Objectives, Different Generation Paths | CMU 10-423 Topic Guide
Diffusion and VAEs share probabilistic ideas without sharing the same generation procedure. Diffusion chains noise steps; a VAE approximates a latent posterior with an encoder and reconstructs through a decoder. This guide connects those ideas while directing each mechanism to its own lesson.