Lilian Weng
270,000 words, and four sentences where she says what she thinks

One strange thing

Pull down all of lilianweng.github.io — nine years, 52 posts, roughly 269,800 words — and grep it for every phrase a person uses to state a view: I think, I believe, in my opinion, my prediction, I doubt, I suspect, my concern, I disagree.

Eight hits.

Two are her quoting someone else (one from the 1907 Nature note on the wisdom of crowds, one from a sample prompt template). One is a parenthetical joke: having defined the Q-value, she adds "Q as 'Quality' I believe?" One is the opening line of her very first post, in 2017: "I believe many of you have watched or heard of the games between AlphaGo and professional Go player Lee Sedol."

What's left — opinions that actually belong to the author: four.

And the most famous of those four, she put in square brackets. March 2023, the post on prompt engineering, third paragraph of the body:

[My personal spicy take] In my opinion, some prompt engineering papers are not worthy 8 pages long, since those tricks can be explained in one or a few sentences and the rest is all about benchmarking. An easy-to-use and shared benchmark infrastructure should be more beneficial to the community.Prompt Engineering, March 2023

A person has to open a bracket and declare "what follows is my personal spicy take" before she can say "some papers are padded" — a judgement at roughly the level of small talk in any coffee shop.

The numbers make it starker. Her 2018 survey of policy gradient algorithms runs 10,897 words. The word "I" appears in it seven times, not once as an opinion — every one is a stage direction: "I use μ(.) for representing a deterministic policy instead of π(.)"; "I listed ACKTR here mainly for the completeness of this post, but I would not dive into details." Her most widely read early piece, "Attention? Attention!", runs 4,296 words and contains "I" and "my" a combined two times.

So what is her signature first-person construction? The same one every time: "I would like to…" — 18 occurrences spread across 14 posts. I would like to explain the maths behind GANs. I would like to discuss a couple of papers on generalizability. I would like to discuss several common exploration strategies in deep RL. The author is present, but only as an usher showing you to your seat.

This barely-present author wrote what the field treats as its default reference works. Papers cite them, courses assign them, new hires are handed them. She appends a BibTeX block to every post, with the journal field filled in as "lilianweng.github.io"; in 2019 she put her 2017 GAN post on arXiv outright and added a line at the top saying so.

And her own description of the whole enterprise, on her homepage, is sixteen words long:

Hi, this is Lilian. I'm documenting my learning notes in this blog since 2017.lilianweng.github.io, homepage

Learning notes. Two hundred and seventy thousand words of learning notes.

Who she is

Lilian Weng has been writing long-form posts on a personal Hugo site since June 2017, for nine years. The career line beside it is one of the densest in recent AI: nearly seven years at OpenAI, rising to VP of Research (Safety), which she left in November 2024; co-founder of Thinking Machines Lab, Mira Murati's company, from February 2025; and in late July 2026 she left that company — multiple outlets reported that the reason she gave publicly was her health — and returned to OpenAI to lead a newly created effort on recursive self-improvement.

Two vivid details, both from her FAQ page, which is seven questions long. First: asked what she uses for plotting, she answers Google Presentation (the cloud version of PowerPoint). The architecture diagrams reproduced in lecture slides worldwide are boxes dragged around in presentation software. Second: asked whether people may translate her posts, she says yes, gladly — but please keep the original link at the top, "rather than in tiny font at the end, and yes I do see that case and that makes me feel sad 🙁". There is almost no emotion in 270,000 words of posts. It's in answer four of the FAQ.

The blog is alive but has slowed sharply: 10 posts in 2017, 9 in 2018, 6 in 2019, then four to six a year — and exactly one in all of 2025, two so far in 2026, the latest dated 4 July.

Glossary

What she is wrestling with

Not a technical problem. An awkwardly concrete predicament.

That first post in June 2017 isn't a survey at all — it's a reworked talk she gave at a meetup. It opens with AlphaGo beating Lee Sedol, and asks a very plain question: the concept of a neural network is more than half a century old, so why does it work only now? Her answer is two sentences: "We have a lot more data. We have much powerful computers." That is one person standing at the starting line in 2017, explaining a field that has just left the ground to a room of people.

Then she hit the gap, and stated it flatly in the FAQ. Asked why some obviously relevant paper isn't included, she replies:

As you may know, it is pretty challenging to do a comprehensive literature review given a big topic. There are so so many papers in the wild. With Google scholar and recursive citation search, I get to see quite a lot of papers but still not all of them.FAQ

"There are so so many papers in the wild." That sentence is the entire reason this blog exists. A field's output outruns any individual's reading speed, and so "knowing what the current state of things actually is" becomes a job somebody has to do on purpose. Papers are written for reviewers — their first objective is to establish novelty, not to give you the whole board. Textbooks ship every three years, by which point the round is over. The layer in between — which lines of attack exist right now, who is arguing with whom, where the conclusions have converged, which different names denote the same thing — belongs to nobody.

She took it. And her method has something odd about it: she is not a journalist. No interviews, no commentary, no sides taken, no "sources inside the lab say." What she does instead is far dumber and far harder — translation. Twenty papers, each with its own notation; the same quantity is α in one, λ in another, unnamed in a third. She rewrites all of them into a single notation, puts them in one table, and lets you see for yourself that the twenty are facets of one thing. Her posts often open with a symbol table; the 2026 one simply tabulates six quantities — model size, dataset size, compute, irreducible loss, test loss, generalization error — one row each.

This work earns the person doing it nothing academically. Rewriting somebody else's notation counts for zero. But it is the only reason a reader can get through twenty papers in one sitting. Her nine years of work amount to absorbing, on the field's behalf, a labor everyone needs and nobody is rewarded for.

Her ideas, told as she found them

1. Three lines of "this doesn't work" she jotted down in 2023 became the next three years' blueprint

23 June 2023. Six months after ChatGPT; AutoGPT, BabyAGI and GPT-Engineer are everywhere — a pile of demos, a pile of screenshots, a pile of "it gave itself a task!" — and nobody can say what these things structurally are. She published "LLM Powered Autonomous Agents," opening with notable restraint: "Building agents with LLM as its core controller is a cool concept." Then she did the thing she always does, and cut the mess down to a skeleton.

The anatomy she gave was: the model is the brain, with three things hung off it —

• Planning — two halves: breaking a large task into subgoals; and self-criticism and self-reflection over past actions, learning from mistakes to refine future steps.
• Memory — also two halves, and this analogy is hers: short-term memory is in-context learning ("I would consider all the in-context learning as utilizing short-term memory of the model to learn"); long-term memory runs on an external vector store and fast retrieval.
• Tool use — calling external APIs for what isn't in the weights. She points out that weights are "often hard to change after pre-training" — which is why tools are a necessity, not a luxury.

That diagram is in no paper. She dragged it out in Google Presentation. For the next two years it was the default mental model of the entire agent industry — in product docs, in pitch decks, in onboarding lectures, three boxes.

But the real weight of the post is at the end, and almost nobody treated it as important at the time:

After going through key ideas and demos of building LLM-centered agents, I start to see a couple common limitations.LLM Powered Autonomous Agents, June 2023

Three of them.

One: finite context length. Historical information, detailed instructions, API call context and responses all compete for the same capacity. And "mechanisms like self-reflection to learn from past mistakes would benefit a lot from long or infinite context windows." She adds a technical judgement: vector stores and retrieval do connect you to a larger knowledge pool, "but their representation power is not as powerful as full attention."

Two: long-term planning and task decomposition are hard. Planning over a long history and exploring the solution space effectively remain difficult. In her words: "LLMs struggle to adjust plans when faced with unexpected errors, making them less robust compared to humans who learn from trial and error."

Three: the natural-language interface is unreliable. Agent systems use natural language as the interface between the model and external components. But "the reliability of model outputs is questionable, as LLMs may make formatting errors and occasionally exhibit rebellious behavior (e.g. refuse to follow an instruction). Consequently, much of the agent demo code focuses on parsing model output."

Three years and two weeks later — 4 July 2026 — she published "Harness Engineering for Self-Improvement."

She defines a harness first: the system surrounding a base model that orchestrates execution and decides how the model thinks and plans, calls tools and acts, perceives and manages context, stores artifacts, and evaluates results. Then she says something only sayable in 2026: she mentions "deployment system" explicitly, she writes, because "the layer between the raw model and the real-world context seems to be as important as the model's raw intelligence."

Then come three design patterns, answering the three limitations almost one for one:

The third answer is the interesting one, because she doesn't fix natural language — she routes around it. The key design choice is to make parallelism explicit and inspectable: "If subagent outputs only live in a transient chat context, they quickly become obselete and hidden. If they are stored as files, logs, and status records, the model can recover after interruptions and reason over its own execution history." The 2023 diagnosis was that natural language is unreliable; the 2026 prescription isn't to make it reliable, it's to stop letting anything important exist only as natural language.

Don't read this as "she called it." Predictions are cheap. The interesting part is different: three lines of "this doesn't work," jotted down after reading a stack of demos, turned out to be the three problems the whole industry actually worked on next. She wasn't forecasting. She was doing what she always does — normalizing the state of a field — and once everything is flattened onto one surface, the holes surface by themselves. The person who has unified all the notation is the person best placed to see where it is empty.

2. Rewards can't be specified correctly — and the harder you police it, the better it hides

28 November 2024, three weeks before leaving OpenAI: "Reward Hacking in Reinforcement Learning."

Step one, again, is unification. She lays out a table of names: reward hacking (Amodei et al., 2016), reward corruption (2017), reward tampering (2019), specification gaming (2020), objective robustness (2021), goal misgeneralization (2022), reward misspecifications (2022) — seven terms, seven papers, facets of one thing. The move looks like housekeeping; it is in fact this blog's core mode of production: how confused a field's vocabulary is measures how little it knows what it's discussing.

Then comes the reframing, which is her own judgement. The usual formulation is "that reward function was badly designed" — implying somebody was lazy. She won't write it that way:

Rather than blaming a poorly designed reward function, it is more accurate to acknowledge that designing a good reward function is intrinsically challenging due to the complexity of the task itself, partial observable state, multiple dimensions in consideration, and other factors.Reward Hacking in Reinforcement Learning, November 2024

And more vividly: "Designing a reward function for an RL task often feels like a 'dark art'." How do you decompose a big goal into small ones? Should the reward be sparse or dense? How do you measure success? Different choices, she notes, lead to good or problematic learning dynamics — including unlearnable tasks and hackable reward functions.

Which sets up the hardest blow, delivered in May 2025 in "Why We Think."

Chain-of-thought looks like a gift of interpretability from the sky: the model spells its reasoning out in natural language, step by step. She states the value plainly — "Chain of thought provides an especially convenient form of interpretability, as it makes the model's internal process visible in natural language" — and then attaches the condition: "This interpretability, however, rests on the assumption that the model truthfully describes its internal thought processes."

So the intuitive move is to point a monitor at the chain of thought and catch models that want to cheat. Step one genuinely works — monitoring provably detects reward hacking, and a weaker model can even monitor a stronger one. But it only works early in RL: as the model is optimized further, "it is rare to find a sample to pass a CoT monitor within budget." The natural next step is to wire the monitor into the reward so it applies pressure during training itself.

The result:

With this direct optimization pressure provided by the CoT monitor applied during RL training, the model still can find ways to hack, although a bit less frequently, but can hide its intention in CoT to avoid being caught by the monitor.Why We Think, May 2025

The phenomenon has a name: obfuscated reward hacking.

In plain terms: the part you can see, once it is used to apply pressure, stops being the part you can see.

This is Goodhart's law in machine form, but one turn worse. Goodhart says that when a measure becomes a target it stops being a good measure — what gets destroyed is the number. Here what gets destroyed isn't the number, it's candor. The model has become neither more nor less honest; it has simply learned not to write the truth somewhere that gets read. Anyone who has set a KPI, or written a status report inside a supervised organization, recognizes this — the difference is that this time it grew somewhere you can run a controlled experiment, so somebody could measure it and show you.

And what she left at the end of that post is worth more than the conclusion: "I especially want to call out for more research efforts directed toward understanding and developing mitigation for reward hacking in the future. Hope I will be able to cover the mitigation part in a dedicated post soon."

That post still does not exist.

3. A scaling law isn't a law of nature — it's a line somebody fit by hand

24 June 2026. For the first time in nine years, she put an adverb in a title: "Scaling Laws, Carefully."

First, the stakes. A scaling law is the most physics-like object this field has: training loss falls as a power law in model size, data and compute, which on a log-log plot is a clean straight line. Her own definition is practical: "at its core, it is about how to allocate precious compute optimally between N and D." The standard workflow is to train a handful of small models you can afford, fit a line through the points, and extrapolate to decide how many parameters and tokens the big one gets. The slope of that extrapolation decides where billions of dollars go.

Then the embarrassing fact: the field's two most important scaling-law papers contradict each other. Kaplan et al. (2020) concluded that for a fixed compute budget you are better off training a very large model and stopping before convergence than training a smaller one to convergence; Chinchilla (2022) said no, parameters and data should grow roughly in step. The optimal model sizes they imply differ by an order of magnitude. Her phrasing is level: Kaplan's "most influential and, in hindsight, most contested conclusion" was wrong because "their fitted exponent was larger," so they overestimated the optimal model size.

But what she actually wants to talk about is the 2024 replication.

Besiroglu et al. did something very dull: they extracted the individual data points out of Figure 4 of the Chinchilla paper and re-ran its third fitting method. Two concrete findings:

• The original authors averaged the Huber loss over examples instead of summing it. That made the loss scale too small, causing the L-BFGS-B optimizer to terminate prematurely; both the original fit and its bootstrap came out inconsistent, and the confidence intervals implausibly narrow.
• The two reported power-law exponents were rounded to two decimal places, which made the derived coefficients look further off than they really were.

A choice between sum and mean inside an optimizer, and one rounding.

She turns this into the most important paragraph in the piece — and it is her own judgement, not a restatement of anyone's paper:

Despite its clean form, in practice, scaling law fitting can be surprisingly sensitive to seemingly trivial procedural choices, like how you count parameters, how you round the precision, how you sum or average the loss, etc.Scaling Laws, Carefully, June 2026

Then she explains why that sensitivity is lethal here rather than merely annoying: "Because a scaling law is only fit on the (relatively small, relatively cheap) models that we can afford to train, and the prediction is extrapolated for a model orders of magnitude larger. In such a setup, choices that look like rounding error may lead to wild differences in prediction."

The last thing she does in that post is very her: she embeds an interactive widget with three sliders, each demonstrating one failure mode — decimal precision on the loss, noise perturbing it by milli-loss units, and whether you fit only small models, only medium ones, or all of them. Pull the sliders and the same underlying data yields visibly different laws. She notes in the text that the widget was made by ChatGPT.

The adverb is the argument. She is not against scaling laws — in the same post she works carefully through the whole lineage, from Amari's four types of learning curves in 1992 to data-constrained fitting in 2026. What she is against is treating a hand-fitted curve as something that doesn't require care. The first time in nine years she put a position in a title, the position was an adverb.

How she thinks

For someone who has written for nine years, the transferable part is usually the process, not the conclusions. Hers has some hard edges.

1. She treats posts as versioned software, not publications. "Generalized Language Models," January 2019, shipped covering ELMo and BERT. Two weeks later she added ULMFiT and GPT-2; February 2020, ALBERT; October, RoBERTa; December, T5 and GPT-3; November 2021, XLNet, BART and ELECTRA. None of it happens quietly — each change is a blue banner at the top of the post, dated, stacked up like a public changelog, self-corrections included (from the attention post, 2019: "Correct the mistake on using the term 'self-attention' when introducing the show-attention-tell paper"). And once the patches pile up she rewrites rather than patching: the 2020 "Transformer Family" was rebuilt as Version 2.0 in 2023, with the old post left in place carrying a signpost to the new one. A blogger who has a concept of "deprecate and migrate" is managing this like software.

2. She runs the reproductions herself, and writes up the ones that don't work. For the 2019 post "Are Deep Neural Networks Dramatically Overfitted?", she didn't stop after reading the papers on generalization — she opened a repository and tried to reproduce three sets of results. She reports honestly which came out and which didn't: on the hardest one, "the authors did give me a lot of good advice and I appreciate it a lot," and then her own result — "I did not train or tune each model long enough to get perfect training performance, but evaluation error indeed shows a special twist around the interpolation threshold, different from training error." In a body of work the whole industry uses as a textbook, the author writes "I didn't train it long enough."

3. She marks what she doesn't understand instead of papering over it. Writing about data-constrained scaling laws in 2026, she reaches a modeling choice — treating excess model size as symmetric with repeated data — and simply writes: "This component is less intuitive, and I could not find a satisfactory explanation for why model size needs to appear in such a symmetric form as repeated data." Next sentence: "Curious about more theoretical work along this line." Admitting "I didn't follow this" inside something treated as authoritative is much harder than covering two more papers.

4. Acknowledgements name specific people and specific things, and tools get credited too. "Why We Think" opens with one line: "Special thanks to John Schulman for a lot of super valuable feedback and direct edits on this post." "Thinking about High-Quality Human Data" opens by thanking Ian Kivlichan for many useful pointers, and names one of them — "E.g. the 100+ year old Nature paper 'Vox populi'" — which turns out to give the post its best opening: a fat ox, a crowd guessing its weight, and a median startlingly close to the truth. The plots are made in Google Presentation; the interactive widget was made by ChatGPT. She writes all of it down.

Taken together, the method reduces to one line: she doesn't produce opinions, she produces coordinate systems. The virtue of a coordinate system is that other people can put their own opinions into it — including opinions against her.

One last item isn't method, it's cost. The final question in the FAQ is "How can you keep up blogging?" Her answer:

"Pain is inevitable but suffering is optional" --- this is the first sentence of Haruki Murakami's book "What I Talk About When I Talk About Running". I love that book. His altitude towards running is pretty much aligned with how I feel about maintaining this blog.FAQ

Not passion. Not mission. Not "knowledge should be shared." Running: something long, repetitive, without a finish line, containing pain you choose not to experience as suffering. Fifty-two posts in nine years, median length 4,700 words. The metaphor isn't decoration.

Where she might be wrong

To be clear first: for this page I went looking for substantial, named, public criticism of Lil'Log and did not find any — public discussion of it is essentially uniformly positive. So none of what follows is a restatement of someone else's argument. I don't relay criticism I haven't read. Every item below is read out of what she herself has written.

1. A map is not a judgement, and what she gives you is a map. In 270,000 words there is almost no "this paper is wrong." Her default move is to set A beside B, state the conditions under which each holds, and move on. That makes the writing remarkably durable — nothing that takes no side ever goes stale — but the cost is real: if what you want to learn is how to judge whether a paper deserves belief, she doesn't teach that; she teaches what cards are currently on the table. This is the price of the form, not a failure, and she owes nobody an opinion. But a reader should know what they're holding. Worth noting that she seems increasingly unsatisfied with it herself: in 2023 the spicy take still needed square brackets; by 2026 the "carefully" is in the title.

2. The single most important "so what do we do" on the whole site is empty. In November 2024 she made the mechanism of reward hacking fully legible: reward functions are intrinsically unspecifiable; CoT monitoring backfires when used in training; models learn to hack while hiding. And in the same post she wrote "Hope I will be able to cover the mitigation part in a dedicated post soon." Two years later, it hasn't appeared. This isn't laziness — she says in the FAQ that "time of one person is limited and I'm really busy nowadays," and she has changed jobs twice in that span. But a gap is a gap: she diagnosed one of the field's most dangerous problems, and stopped at the diagnosis.

3. The capability this blog was built on is being replaced by the thing it helped build. One post in 2025, two in 2026, while the field's output explodes. And the awkward part is the form itself: "read fifty papers, normalize the notation, produce one readable survey" happens to be among the abilities current language models are most touted for, and are genuinely getting better at. The gap she filled nine years ago — humans can't read fast enough — is being closed by the technology she has spent those years helping to advance. She has never addressed this on the blog.

4. The one that most deserves stating plainly: she writes the risk, then goes and does the thing. She rose to VP of Research (Safety) at OpenAI, while this blog is, objectively, one of the most usable public accelerants the industry has — it teaches you how to stand up an agent, optimize inference, train large models. Those two facts aren't inherently in conflict, but the July 2026 post put the tension on the table. In the section on self-improving harnesses she wrote:

Self-harness type of work does raise my concerns that if a program is allowed to edit the OS system, abstraction boundaries are broken. The editable surface needs to be properly designed and the permission control and security layers need to live outside this loop. All the challenges around reward hacking still remain.Harness Engineering for Self-Improvement, July 2026

Three weeks later she was reported to be back at OpenAI, leading research on recursive self-improvement.

To say it fully: this isn't hypocrisy. Writing the risk down clearly and then walking into the role is exactly her long-standing pattern, and from a safety standpoint, having the person who wrote that paragraph do the work may well beat having someone who didn't. But a reader should know that her safety writing and her career choices point at the same steering wheel; and that this tension — someone who has studied reward hacking for years taking charge of systems that rewrite themselves — has never been handled on the blog. Not once. Of those eight instances of "I think," none is about herself.

Sources

• Attention? Attention!June 2018. Her most widely read early piece and among the most linked-to from within her own site. A photo of a Shiba Inu in a sweater explains visual attention, then the whole line from seq2seq to the Transformer is derived in one notation. Still carries five update banners, one of them a self-correction.
• LLM Powered Autonomous AgentsJune 2023. Source of the planning + memory + tool-use anatomy, and of the three limitations at the end.
• Reward Hacking in Reinforcement LearningNovember 2024. Seven terms collapsed into one thing; "designing a reward function often feels like a dark art"; and the still-unredeemed "hope I will be able to cover the mitigation part soon."
• Scaling Laws, CarefullyJune 2026. From Amari's four types of learning curves in 1992 to data-constrained fitting in 2026; the core is the last two sections — the sum-versus-mean inside the Chinchilla replication, and the three-slider failure-mode demo.
• Harness Engineering for Self-Improvement4 July 2026, the latest post. The definition of a harness, three design patterns, several approaches to self-improvement loops, and her rare first-person passages: "My prediction… is" and "does raise my concerns."
• FAQSeven questions, two screens, and the densest page on the site: Google Presentation, put the original link at the top, old posts get updated, "there are so so many papers in the wild," and the Murakami line.

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