A language model does one thing: given the preceding text, output a distribution over the next word. At large enough scale, the way to control it becomes changing the input: a few examples (in-context learning), or asking it to write its reasoning first (chain-of-thought; the example cited in class is one sentence, Let's think step by step, taking an arithmetic task from 18% to 80%). But following instructions and saying 'I don't know' do not come from next-word prediction alone; instruction tuning and RLHF add them.
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.
11-868's RL systems lecture has no slides; the Syllabus lists just one paper, ReaLHF. Assignment 7, on the other hand, is fully public. You train a DistilBERT reward model on Anthropic's HH-RLHF data (40 points), fill in GAE, the PPO loss, and entropy in a VERL-style trainer to fine-tune GPT-2 (40 points), and compare reward distributions before and after RLHF (20 points). The starter trainer never imports the verl package. What you learn is the RLHF dataflow, not VERL's distributed engine.
Lecture 8 of CS224R (Spring 2026) spends a few slides wrapping up offline RL, then asks the question the first seven lectures skipped: where does the reward come from? Games have scores. Real robots, dialogue, and driving usually don't. The slides offer two routes. The first trains a goal classifier on success examples and uses it as the reward, but RL learns to exploit the classifier's blind spots; the fix is to keep adding states the policy visits as negatives, the same structure as a GAN. The second asks people which of two trajectories is better and learns a reward with the Bradley-Terry-style objective log σ(r(τw) − r(τl)), the same method LLM RLHF uses. The lecture's number-one takeaway is one line: rewards can't be taken for granted.
Lecture 9 of CS224R Spring 2026 is a guest lecture by Archit Sharma, with slides adapted from CS224N. The spine is one chain of reasoning. Instruction tuning can't handle tasks with no right answer or errors of unequal weight, so we optimize human preferences directly. Human ratings are expensive and noisy, so we collect pairwise comparisons and fit a Bradley-Terry reward model. RLHF uses that model as the reward and runs policy gradient with a KL penalty. DPO uses the closed-form solution of the KL-constrained problem to write the reward as a log-ratio of policies, which turns the whole thing into a binary classification loss. The last part covers the frontier: reward hacking, verifiable rewards, and AI feedback in place of human feedback.
CS234 Winter 2026 Assignment 3 has five questions worth 94 points. The first three share MuJoCo Hopper: run PPO on a hand-written reward (13), learn a reward model from 10,000 preference pairs and run PPO on it (19 + 8), then learn a policy straight from preferences with SFT + DPO without ever touching the environment (6 + 19). Q4 switches to pure theory: use Hoeffding and a union bound to count how many pulls you need to find an ε-optimal arm (25). Save it until after the next post on bandits. Q5 is stated vs. revealed preferences in a news app (4).
CS234 L8 keeps the inverse RL problem from the previous post but changes the input. Instead of expert demonstrations, a human says "A is better than B." The Bradley-Terry model turns these pairwise comparisons into a reward you can fit with cross-entropy. RLHF runs PPO on that reward model with a KL penalty. DPO shows that the KL-constrained optimal policy has a closed form and rewrites the reward as a log-ratio of policies. Plugged back into Bradley-Terry, the partition function cancels, so you can train the policy on preference data directly, with no reward model. The 2026 slides contain no offline RL, and DPO is now taught in lecture rather than by 2024's guest speakers.
Boaz Barak treats pretraining, SFT, and RL as one operation: push some tokens up, push others down. What differs is whether the data was written by someone else (off-policy) or generated by the model itself (on-policy). Safety training sits on top of the last two stages. It has moved from blanket refusals to Deliberative Alignment, which first uses SFT to teach the model to read a spec inside its chain of thought, then runs RL with a reward model that knows the spec. The other key point: don't put optimization pressure on the chain of thought, or the model learns to cheat without saying so.
Every model in the first 12 lectures learned from training data that people prepared. L13 asks a different question: when there is no right answer, only a signal of how well you did, how does a computer learn? Tsai starts from AlphaGo and Breakout and splits the field in two. Value-based methods learn a Q function that scores each action (Deep Q-Learning, TD, experience replay, ε-greedy); policy-based methods learn the action directly (policy gradient, actor-critic). The second half returns to LLMs: ChatGPT trains a reward model from human rankings and then runs RLHF with PPO, while DeepSeek has the computer check math answers automatically and uses that as the reward. Week 13 homework is the final project proposal.
Hung-Yu Kao's Fall 2025 W8 slides walk from GPT-1 to GPT-3, explain how the Sparse Transformer behind GPT-3 cuts attention cost, and then use InstructGPT to show the gap between continuing text and following instructions. The maximum likelihood objective can't tell a fabricated fact from a slightly wrong synonym, so three extra stages are added: SFT learns how humans write, a reward model learns how humans grade, and PPO optimizes against that grade while a KL penalty keeps the model from drifting too far. The lecture closes with Llama-2: separate safety and helpfulness reward models, context distillation, and GQA for faster inference.
A pre-trained model continues text well and still may not do what an instruction asks. The first half of this lecture covers the two steps of post-training: instruction tuning uses many different tasks to teach the model to read task descriptions, and RLHF then moves the model toward whole responses that people prefer, with a reward model and pairwise comparisons handling the cost of human labor and inconsistent scores, while DPO and KTO drop the reinforcement-learning part. InstructGPT and ChatGPT are these two steps chained together. The second half covers LLM adaptation: models are too large to fine-tune in full, so only a small number of parameters are trained, with Adapter, LoRA, and prompt tuning, and no single method is best on every task.
SFT only teaches a model to imitate good answers; it has no way to say which answers are unacceptable. CME295 Lecture 5 covers how to collect preference pairs, walks through the two steps of RLHF (a reward model trained on roughly 10,000 human labels, then PPO on roughly 100,000 examples), and ends with DPO, which folds the whole RL pipeline into a single supervised loss. The 2026 edition splits this lecture between Lecture 3 (training) and a new Lecture 4 (reinforcement learning).
You don't need to become a researcher to understand AI models systematically. This series starts from what you can see (tokens, context windows) and works up to self-hosting open-source models — 18 articles covering everything you need to choose models, read benchmarks, and estimate costs.
Every LLM goes through three training stages: pre-training reads the internet to learn language, SFT uses example conversations to learn the format, and RLHF uses human preferences to learn what a good answer looks like. The gap between a base model and a chat model is what the last two stages do.
2022 marked NLP’s shift from demonstrating model capabilities toward aligning and controlling them. InstructGPT brought RLHF into the mainstream, Chain-of-Thought showed that prompts could unlock reasoning, and Flan 2022 matured instruction-tuning methodology. ACL and NAACL adopted ARR as their sole review path, exposing infrastructure and reviewer-load problems. ChatGPT launched at year-end and rewrote the rules of NLP research.
2022 was a turning point at major AI conferences. Diffusion models moved from emerging to mainstream, with two NeurIPS Outstanding Papers; Chinchilla rewrote scaling laws; Chain-of-Thought showed that large models could reason; and InstructGPT used RLHF to teach language models to follow instructions. When ChatGPT launched at year-end, these academic topics instantly became global news.
2023 was the first year in which LLMs comprehensively rewrote the AI research agenda. DPO received a NeurIPS Outstanding Paper Runner-Up award, ReAct became an ICLR Oral, and hallucination grew from a marginal term into a major track at every conference. Meanwhile, 3D Gaussian Splatting swept through computer vision after its SIGGRAPH debut, Mamba emerged at the end of the year to challenge the Transformer attention monopoly, and publication volume for traditional NLP pipelines began a clear decline.
The two strongest signals at AI conferences in 2025 were reasoning papers jumping from 47 to 216, a 4.6-fold rise, and agent-related terms exceeding 150 papers with 4.3–11-fold growth. Diffusion moved from breakout topic to infrastructure; RAG became a mainstream enterprise architecture with unusual coverage across all five conferences; state-space models and world models began tracing the early 2020–2021 path of Vision Transformers. Pure prompt-engineering papers encountered reviewer fatigue.
Lecture 8 explains how instruction tuning, preference data, and RLHF turn a pretrained model into an assistant, then derives DPO from winner–loser pairs. Every step converts human judgment into signal—and imports its biases.
Lecture 15 divides post-training into imitation and optimization. SFT extracts pretrained capabilities from instruction-response data; RLHF uses pairwise feedback to bridge demonstrations and preferences. PPO and DPO both inherit data bias, reward overoptimization, and mode collapse.
The dividing line in LLM interviews is whether you've actually used these things. High-frequency topics: BPE tokenization logic and multilingual challenges, pretraining objectives (CLM vs MLM), three levels of fine-tuning (full/LoRA/prompt tuning), RLHF workflow and failure modes, prompting as engineering practice, and the difficulty of LLM evaluation with current methods.
The third reason Go can't be learned with supervision is the interesting one: the ground truth itself is ill-defined — the strongest human doesn't play their best moves every day, and even their best move isn't optimal. The last 15 minutes or so map RLHF fully back onto RL: the agent is the model being fine-tuned, the action is the next token, an episode is one full generation, and the reward is extremely sparse.