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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.
A GAN learns from a discriminator.
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.
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.
A classifier asks which class an image belongs to.
This series uses the public Spring 2026 materials for CMU 10-423/623/723 Generative AI.
Assignment 1 brings sequence modeling and modern attention into code.
Copying a noising equation is not enough to implement diffusion correctly.
Use an original numerical test to check gradients, data, and evaluation alongside the original assignment specification.
Use an original numerical test to check gradients, data, and evaluation alongside the original assignment specification.
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 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.
Attention needs both order information and a manageable memory footprint during generation.
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
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.
A sequence such as “I like cats” is not a bag of independent words.
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 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.
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.