HW4 moves next-token prediction from text to images unchanged: 792 Pokémon sprites at 20×20, each pixel one of 167 color tokens, so one image is a 400-token sequence. Training is next-token prediction; at test time you get the first 60% of an image and the model draws the rest. Grading checks FID and a Pokémon Detection Rate (PDR) together, and the three baseline hints go from "run the sample code" to "tune hyperparameters" to "switch to Llama or Mistral". The spec, Colab, Kaggle notebook and dataset are public, but JudgeBoi returned 502 on 2026-09-30, so outside readers cannot get official FID or PDR scores.
HW5 fine-tunes Llama-3.2-1B-Instruct on GSM8K with LoRA, then uses harmful AILuminate prompts to check whether it still refuses. Math accuracy and safety rate must clear the bar together, so the real question is how to fine-tune without washing out safe behavior. The PDF, a 34-cell Colab, and a Kaggle version are public, and the strong baseline is estimated at 14 hours on a T4. The JudgeBoi grader returned 502 on 2026-09-30, so outside readers have to build their own safeguard evaluation.
HW6 involves no model training and is answered entirely on NTU COOL. Six points come from 16 multiple-choice questions on four papers (ROME, MEND, MEMIT, WISE). Four points come from swapping the Colab's fine-tuning for ROME on GPT2-XL: single editing (pick your own fact, write five kinds of test prompts) and multiple editing (10 and then 80 CounterFact examples, then MEMIT), reporting efficacy, paraphrase, neighborhood, and portability scores. The slides and the 47-cell Colab are public, but the quiz questions and answers live only on COOL.
HW7 hands you two models fine-tuned from Mistral-7B-v0.1: shisa-gamma-7b-v1, strong in Japanese, and WizardMath-7B-V1.1, strong in math. You may only merge them at the parameter level (no further training, no MoE or ensembles), and the merged model has to answer 20 Japanese math questions written by a TA. Part 1 (60%) is tuning the method, weights, and density in mergekit, with simple and strong baselines at 50% and 75% accuracy. Part 2 (40%) is 8 multiple-choice paper questions. The spec, Colab, and Kaggle notebook are public, but JudgeBoi returned 502 on 2026-09-30 and the paper questions live on NTU COOL, so outside readers can only check accuracy inside the notebook.
HW8 involves no coding and no code submission. The TAs provide a finished Colab that runs Llama-3.2-1B-Instruct on the first 100 GSM8K questions and compares direct inference, Self-Consistency, Self-Certainty, and DeepConf (Confidence), sampling 16 reasoning traces per method. You read three papers, run the notebook, and answer 20 questions on NTU COOL: 18 about the papers and 2 about the Colab results. The prerequisite is Lecture 7 (Reasoning) of Lee's 2025 course. All questions are printed in hw8.pdf in Chinese and English, and the Colab is publicly downloadable. Only the COOL quiz and grades need an NTU account.
HW9 has 19 questions worth 10 points, answered only on NTU COOL with no code submission. The first 16 cover four papers — DDPM, Flow Matching, Rectified Flow, and MeanFlow — ending with questions that compare their training signals and few-step generation. The last 3 require the Colab: train two small MLPs on a 2D Swiss roll, one Flow Matching model that learns instantaneous velocity (always evaluated with 50 Euler steps, converged at Histogram JS ≤ 0.10) and one MeanFlow model that learns average velocity (always one-step, ≤ 0.40). Then compare 1 step vs 1 step, Flow Matching across Euler step counts, and Euler vs RK4 at equal steps and at similar compute. The PDF includes a generative-modeling tutorial that skips most of the math, and every question is published in Chinese and English. Outside readers miss only the COOL grading and answers.
KV Cache stores the keys and values already computed so decode does not recompute them, but every token costs memory. For Gemma 2 27B that is about 0.72MB per token, so an 80GB A100 holds only about 114k tokens. Hung-yi Lee then walks through ways to shrink it: let queries share keys and values (MQA, GQA), compress keys and values into one vector without ever decompressing (MLA), limit the attention span (Sliding Window, StreamingLLM), and drop keys and values nobody attends to (Scissorhands, H2O). He ends with cross-conversation prompt caching: it only hits when the prefix is identical, so a system prompt should put stable content first.
The first lecture of Hung-yi Lee's ML 2026 breaks OpenClaw into five questions: how an agent knows who it is, how it uses tools and SKILLs, how it remembers, how it runs on a schedule, and how it keeps working on its own for a long time. Every answer comes back to one fact: the language model only predicts the next token and starts fresh every turn. Identity, memory, and SOPs are all text files that OpenClaw puts into the prompt, or files the model reads and writes through tools. This post walks through the 60-slide intro.pdf and the lecture recording, including the defenses the slides recommend.
Self-attention on its own cannot tell "you hit me" from "I hit you", so the model needs position information from somewhere else. Hung-yi Lee's lecture goes from sinusoidal absolute positions to ALiBi and T5's relative biases, then to RoPE, which Llama, Qwen and Gemma all use. The second half covers train-short-test-long: RoPE breaks when it rotates to angles it never saw in training, which led to Position Interpolation, NTK-Aware scaling, YaRN, Dynamic Scaling and LongRoPE. The final twist is NoPE: causal attention in a decoder-only model already carries position information, and you can even drop the positional embedding after training.
This lecture asks whether a model can catch and fix its own errors with no human in the loop. Hung-yi Lee splits the approaches into three routes. Change inference: the whole contrastive decoding family builds a version of the model likely to be wrong and subtracts it, and the methods differ only in how that wrong version is made. Change the workflow: appending "check again" sometimes helps but is unstable, external feedback beats self-reflection, and under a fixed compute budget, sampling more answers and voting often wins. Change the weights: teaching self-correction directly runs into "after training, the model makes different mistakes," which is why the field moved to RL. Whether RL teaches new abilities or just makes existing paths more likely is still being debated.
Hung-yi Lee opens his May 8 lecture by admitting that "self-improving AI" has no clear definition: it is a process of humans gradually letting go. He splits machine learning into three steps and checks where the "I" can be replaced by AI. Answers can come from the model's own self-corrections, reward shaping can be written by an LLM, the loss can be set by the model itself (scores, majority vote, entropy), and even the questions can come from a proposer model. But experiments keep showing that with no human at all, progress plateaus or the model trains itself into the ground. A strong AI can already train a weaker one, just not better than humans do. His verdict: in May 2026, AI is "still standing at the bank of the Rubicon."
Part 1 was about an AI setting its own loss and updating its own parameters. Part 2 fills in the other half: AI Agent = Harness + LLM, and the harness can grow too. You can't take a gradient through a harness, so the usual move is to hand it to a language model as a rewriter and keep a pool of candidates, much like a genetic algorithm (OPRO, GEPA, Darwin Gödel Machine; DSPy if you want a ready-made tool). Three extensions follow: updating harness and parameters together beats updating either alone; when the goal changes you have to choose between discarding everything and carrying everything, and editing a harness can cause forgetting too; and the update rule itself can be updated (HyperAgent, Gödel Agent, SEAL), which is meta learning. Hung-yi Lee closes with a new analogy — parameters are genes, context is the neurons — then argues that today's agents lack intrinsic motivation, and that the likeliest source of runaway growth is a gap between the goal humans meant and the goal the AI inferred.
Plugin Extensions, announced at DevDay 2026 (Sep 29), let a plugin put a home in the ChatGPT sidebar, a panel beside the conversation, and a viewer for its file types — up to three entrypoints per MCP App. A plugin is now Skill + MCP + external services + UI, which adds a new product question: which capabilities should ship as a plugin an agent can install?
National Taiwan University spreads its AI/ML courses across Electrical Engineering and Computer Science, and an official 'Machine Learning and AI' specialization stacks them into four levels. Hung-yi Lee's courses are the most open: ML 2026 Spring and Intro to GenAI and ML 2025 Fall publish slides, recordings, homework PDFs, and Colab notebooks, with only grading held back. Hsuan-Tien Lin's lectures are fully recorded and his Fall 2024 HW0–HW7 remain on the course page; Yun-Nung Chen's lectures are fully recorded, but most homework is public only as walkthrough videos; and since August 2025 Coursera only lets free learners watch the first module.
Most public AI courses in Taiwan outside NTU come from TAICA, an alliance set up by the Ministry of Education. Each semester's course list says where every flagship course streams, and courses that stream on YouTube are usually watchable by anyone. Two courses are complete enough to self-study: Hung-Yu Kao's Natural Language Processing at NTHU (Fall 2025) and Yen-Lung Tsai's Generative AI at NCCU (Spring 2025), both A3. Wei-Ta Chu's Introduction to AI at NCKU, Ping-Hsuan Han's Human-AI Interaction at NTUT, and Min-Chun Hu's Robotic Navigation and Exploration at NTHU have full recordings but keep assignments on NTU COOL, so they rate A2. NYCU's TAICA courses are taught in English; the Deep Learning recordings are not publicly listed and Physical AI has just started, so neither made the main table.
Lecture 7 of CME295 (2025) patches three LLM gaps: RAG fixes knowledge frozen at training time with a two-stage retrieve-then-rerank pipeline; tool calling fixes the inability to act by having a backend execute the function call the model writes; agents chain those calls with ReAct's observe-plan-act loop. The 2026 edition renames it AI Agents and adds context compaction, harness optimization, coding agents, and skills, the biggest rewrite in the course.
The 2026 syllabus for CME295 Lecture 6 (November 6, 2026) lists seven topics. Tool calling, MCP and retrieval were already covered in the 2025 Lecture 7; the genuinely new ones are context compaction, harness optimization, coding agents, and skills/plugins. This pre-lecture edition explains those four using engineering posts from Anthropic and OpenAI, the MCP 2026-07-28 spec, and the Meta-Harness paper.
CME295 is a two-unit Stanford course with no homework; your grade is the midterm and the final, 50% each. The 2025 edition's nine lectures are fully public: videos, slides, and both exams with solutions. The 2026 edition rewrites the agent lecture around context compaction, harnesses, coding agents, and skills, and adds three full lectures on LLM systems, reinforcement learning, and Diffusion LLMs.
The last CME295 lecture packs 128 slides into three parts: an eight-picture recap of the quarter, how Transformers handle images (ViT and two ways to build a VLM), and masked diffusion LLMs that emit several tokens per step, followed by what comes next in research and applications. It is not on the exam; the 2026 edition turns diffusion LLMs into a lecture of their own and refocuses Lecture 9 on multimodality.
The 2026 edition of CME295 gives diffusion LLMs a full lecture (Lecture 8, November 20), with five listed subtopics: continuous, discrete and masked diffusion, training, and inference. This pre-lecture edition works from the original papers (DDPM, D3PM, SEDD, MDLM, LLaDA and others): continuous noise costs about 64x the compute on text, and the [MASK] absorbing state won out; the training objective is a masked cross-entropy weighted by 1/t; the speed comes from filling several positions per step, yet LLaDA's main results decode one token per step, and Fast-dLLM needs a confidence threshold plus an approximate KV cache to reach up to a 27.6x speedup.