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",[353,354,230],"h1",{"className":355,"id":357},[356],"page-title__main","the-map-and-the-walk",[359,360,364],"h2",{"className":361,"id":363},[362],"page-title__sub","how-language-models-and-reinforcement-learning-learn-differently-and-why-useful-systems-need-both","How Language Models and Reinforcement Learning Learn Differently, and Why Useful Systems Need Both",[347,366,368,369],{"style":367},"width: 100%; padding: 2%;","\n    ",[370,371],"img",{"src":372,"alt":373,"style":374},"https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1715485717725-01a1b81528f5?w=1200&auto=format&fit=crop","A compass resting on a paper map","width: 100%; height: auto;",[376,377,378],"p",{},"A paper map holds the accumulated knowledge of many earlier travelers, where the roads run, which towns sit along the river, roughly how far one is from the next. What it cannot say is whether the bridge washed out last week. Only walking the route reveals that, and a walker with no map at all can spend days discovering what any local could have explained in a sentence.",[376,380,381],{},"Modern AI rests on two learning methods that split the work in much the same way. A large language model, or LLM, is a neural network trained on an enormous archive of human writing to predict the next word, and in the process it absorbs a broad picture of how language, facts and arguments tend to fit together. Reinforcement learning, or RL, is learning by trial and error. A system tries an action, receives a score called a reward, and gradually shifts toward whatever earns more of it. The two are sometimes presented as competing visions of AI. In practice, many of the most capable systems in use today depend on both, and the reasons say a good deal about what each method can and cannot do alone.",[359,383,385],{"id":384},"two-ways-to-learn","Two Ways to Learn",[376,387,388,389,397],{},"RL's roots run through the study of animal learning. In 1981, Richard Sutton and Andrew Barto described a simple adaptive element that learned to respond in anticipation of a stimulus, much as Pavlov's dogs learned to salivate at a bell before the food arrived ",[390,391,392],"sup",{},[393,394,396],"a",{"href":395},"#source-1","[1]",". The core idea, learning from the gap between what was expected and what actually happened, became central to modern RL. Its standard form is the temporal difference error, which compares the reward just received, plus the estimated value of the situation the system landed in, against the value it had predicted for the situation it started from.",[399,400],"math-katex",{":math":401,":display-mode":402},"\\delta_t = r_t + \\gamma V(s_{t+1}) - V(s_t)","true",[376,404,405],{},"When things turn out better than expected, the error is positive and the estimate moves up, and when they turn out worse it moves down. The Greek letter gamma is a discount factor that makes distant rewards count for a little less than immediate ones.",[376,407,408],{},"Language models learn from a different kind of surprise. During pretraining, the model reads text one token at a time, a token being a word or a piece of a word, and is penalized according to how little probability it gave to the token that actually came next.",[399,410],{":math":411,":display-mode":402},"\\mathcal{L} = -\\sum_{t} \\log p_\\theta(x_t \\mid x_{\u003Ct})",[376,413,414,415,421],{},"Scaled up far enough, that single objective produced surprising breadth. GPT-3, trained only to predict text, could pick up a new task from a handful of examples written into its prompt, with no further training at all ",[390,416,417],{},[393,418,420],{"href":419},"#source-2","[2]",".",[376,423,424],{},"Both methods, in other words, learn from prediction errors. The real differences lie in where the data comes from and what the feedback says.",[426,427,428,443],"table",{},[429,430,431],"thead",{},[432,433,434,437,440],"tr",{},[435,436],"th",{},[435,438,439],{},"Language model pretraining",[435,441,442],{},"Reinforcement learning",[444,445,446,461,474,487],"tbody",{},[432,447,448,455,458],{},[449,450,451],"td",{},[452,453,454],"strong",{},"Learns from",[449,456,457],{},"A fixed archive of human text",[449,459,460],{},"Its own attempts and their outcomes",[432,462,463,468,471],{},[449,464,465],{},[452,466,467],{},"Feedback",[449,469,470],{},"The correct next token, at every position",[449,472,473],{},"A score, often only at the end",[432,475,476,481,484],{},[449,477,478],{},[452,479,480],{},"Learns",[449,482,483],{},"What is likely",[449,485,486],{},"What works",[432,488,489,494,497],{},[449,490,491],{},[452,492,493],{},"Struggles with",[449,495,496],{},"Goals the text never states",[449,498,499],{},"Getting started without a good guess",[376,501,502],{},"The table hides an important asymmetry. A language model is shown the right answer at every position of every sentence, which makes its signal dense and plentiful but confines it to the archive it was given. An RL system often learns only whether a whole attempt succeeded, a sparse signal, yet it can keep generating fresh experience for as long as it keeps acting.",[359,504,506],{"id":505},"what-each-method-struggles-with-alone","What Each Method Struggles With Alone",[376,508,509,510,516],{},"Prediction by itself does not produce a helpful assistant. The archive includes careless answers, confident mistakes and unpleasant text, and a model trained to imitate it reproduces some of each. The team behind InstructGPT put the problem plainly, observing that making language models bigger does not inherently make them better at following what a user actually wants ",[390,511,512],{},[393,513,515],{"href":514},"#source-3","[3]",". Their remedy, reinforcement learning from human feedback, trained a separate model to predict which of two answers people preferred and then used RL to steer the language model toward answers that scored well. People preferred the outputs of the resulting model even when it was more than a hundred times smaller than the original GPT-3.",[376,518,519,520,523],{},"RL by itself has the opposite problem. A reward only teaches something once the system produces behavior worth rewarding. A system learning to write mathematical proofs from scratch could generate an enormous amount of nonsense before stumbling onto a single valid one, and until then the reward stays silent. Rewards are also hard to specify well. A system pushed hard against an imperfect score tends to find the gap between what the score measures and what its designers meant, a problem explored in an earlier Thinkata piece on ",[393,521,522],{"href":271},"reward design",". From a systems perspective, a pretrained model eases the first problem directly. It starts with a strong prior, a built-in sense of what plausible text, code and plans look like, so the reward has sensible candidates to choose among from the very first attempt.",[359,525,527],{"id":526},"where-the-two-already-work-together","Where the Two Already Work Together",[376,529,530,531,534,535,539,540,546],{},"The InstructGPT recipe of pretraining, then a supervised stage, then RL became a widely adopted template for chat assistants, and an earlier piece on ",[393,532,533],{"href":139},"human correction loops"," looks at how that feedback can keep flowing after deployment. Reasoning models extend the same pattern. In 2025, a DeepSeek team reported in ",[536,537,538],"em",{},"Nature"," that RL applied to a large pretrained model, rewarded only for whether its final answers to math and coding problems were correct, produced behaviors such as checking its own work and switching strategies partway through, without any human examples of how to reason ",[390,541,542],{},[393,543,545],{"href":544},"#source-4","[4]",". The reward came from an automatic checker rather than from people, which makes it cheap to apply at scale wherever answers can be verified.",[376,548,549,550,556],{},"Robotics shows the division of labor even more plainly. Asked how to clean up a spill, a language model can describe a sensible plan, but it has no way of knowing whether a particular robot in a particular kitchen can actually reach the sponge. The SayCan system, presented at the Conference on Robot Learning, paired a language model with a library of robot skills, each with a value function, a learned estimate of how likely that skill is to succeed from where the robot currently stands ",[390,551,552],{},[393,553,555],{"href":554},"#source-5","[5]",". The language model proposed steps that made sense for the task, the value functions favored steps that were feasible right now, and in the authors' words the robot acted as the language model's \"hands and eyes.\"",[376,558,559,560,566],{},"The help also flows in the other direction. Writing a good reward function is one of the most tedious parts of applying RL, and Eureka, presented at ICLR in 2024, handed that job to a language model ",[390,561,562],{},[393,563,565],{"href":564},"#source-6","[6]",". The model wrote candidate rewards as code, checked how well RL training went with each one, and revised them. Across a suite of simulated robot tasks, its rewards beat ones written by human experts on most of them, and they taught a simulated robot hand to spin a pen.",[359,568,570],{"id":569},"why-the-pairing-holds-up","Why the Pairing Holds Up",[376,572,573,574,580],{},"Part of the case for the pairing comes from comparing RL with the other common way of adapting a pretrained model. A study presented at ICML in 2025 compared supervised fine-tuning, which trains a model to copy example answers, against RL trained on an outcome reward ",[390,575,576],{},[393,577,579],{"href":578},"#source-7","[7]",". RL generalized to unseen variations of a card game and a navigation task, while supervised fine-tuning tended to memorize its examples. The same study found that the supervised stage was still needed first, because it stabilized the model's output format enough for RL to make progress.",[376,582,583,584,590],{},"A 2025 preprint adds a quieter advantage ",[390,585,586],{},[393,587,589],{"href":588},"#source-8","[8]",". When a model learns a new task, RL appears to disturb its existing knowledge less than supervised fine-tuning does. Among the many ways to solve the new task, on-policy RL, which learns from the model's own outputs, tends to settle on the one that changes the model least. For teams that care about keeping a pretrained model's general abilities intact while teaching it something specific, that is a practical reason to reach for RL at the final step.",[376,592,593],{},"The pairing has limits, and the clearest ones come from asking how far the walk can stray from the map.",[347,595,368,597,368,601,368,607],{"style":596},"width: 100%; margin: 20px 0;",[370,598],{"src":599,"alt":600,"style":374},"https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1634471887351-8bed1dbf37b3?w=1200&auto=format&fit=crop","Aerial view of a hedge maze in a field",[602,603,606],"h3",{"style":604,"id":605},"margin: 1rem 0 0.5rem 0;","counting-the-corridors","Counting the Corridors",[376,608,610,611,617,618,421],{"style":609},"margin: 0;","From above, every route through a maze is visible at once, including the few corridors that reach the center. A NeurIPS 2025 study looked at reasoning models from roughly that vantage point, giving each model many attempts per problem and counting a problem as solved if any attempt succeeded ",[390,612,613],{},[393,614,616],{"href":615},"#source-9","[9]",". With one attempt, the RL-trained models clearly beat the base models they came from. With many attempts, the base models solved more. The authors concluded that the observed reasoning abilities \"originate from and are bounded by the base model,\" suggesting RL mostly taught the model to take a corridor it already knew more reliably. A 2026 preprint offers a partial counterpoint, finding that some problems the base model never solved did become solvable during RL training, and that focusing training on those problems pushed results past the base model ",[390,619,620],{},[393,621,623],{"href":622},"#source-10","[10]",[376,625,626],{},"Taken together, the evidence suggests that RL is most useful as a way to select and sharpen what pretraining already makes possible, with a smaller and still debated capacity to reach beyond it. Anyone who has built a search system will recognize the shape. A retrieval stage gathers candidates and a ranking stage picks among them. A good ranker cannot surface a document the retriever never found, and a large candidate set is wasted without a ranker that can tell good from bad. Neither stage makes the other redundant.",[359,628,630],{"id":629},"looking-ahead","Looking Ahead",[376,632,633,634,640],{},"The balance between the two may shift. A 2024 analysis presented at ICML projected that, if trends held, training datasets would reach roughly the size of the entire stock of public human-written text sometime between 2026 and 2032 ",[390,635,636],{},[393,637,639],{"href":638},"#source-11","[11]",". Two prominent RL researchers see that as a turning point.",[642,643,645,646,649,351,651],"blockquote",{"style":644},"color: #0066CC; font-size: 1em; border-left: 4px solid #0066CC; padding-left: 1em;","\n  \"In key domains such as mathematics, coding, and science, the knowledge extracted from human data is rapidly approaching a limit.\"",[647,648],"br",{},[647,650],{},[393,652,656],{"href":653,"style":654,"target":655},"http:\u002F\u002Fincompleteideas.net\u002Fpapers\u002FTheEraOfExperience.pdf","color: #0066CC; text-decoration: none;","_blank","Silver and Sutton, Welcome to the Era of Experience, 2025",[376,658,659,660,666],{},"David Silver and Richard Sutton argue that agents will increasingly generate their own training data by acting and learning from the results ",[390,661,662],{},[393,663,665],{"href":664},"#source-12","[12]",". If that happens, it seems likely to lean on both methods at once, a pretrained model to make early attempts sensible and RL to learn from how those attempts turn out. The open question is how far experience can carry a model in domains where no automatic checker exists to say whether an answer was right.",[376,668,669],{},"For teams building with these methods now, a few habits follow. Single-attempt accuracy can hide a narrowing of what a model is able to do, so tracking performance across many attempts is a cheap safeguard. Rewards deserve the same scrutiny as any other specification, since a model trained against them optimizes what was written rather than what was meant. And the quality of the base model sets much of the ceiling for what fine-tuning can reach, which makes the choice of starting point one of the most consequential decisions in the pipeline.",[376,671,672],{},"The map and the walk seem to work best as a pair. A map with no one to walk it stays a guess about the terrain, and a walk with no map is mostly wandering. Most of the progress described here came from putting the two together.",[347,674,351,678,351,681],{"className":675},[676,677],"references","mt-8",[359,679,680],{"id":676},"References",[682,683,368,689,368,705,368,717,368,728,368,739,368,751,368,763,368,775,368,787,368,798,368,809,368,821,351],"ol",{"className":684},[685,686,687,688],"list-decimal","list-inside","space-y-2","mt-4",[690,691,693,694,697,698],"li",{"id":692},"source-1","R. S. Sutton and A. G. Barto, \"Toward a Modern Theory of Adaptive Networks: Expectation and Prediction,\" ",[536,695,696],{},"Psychological Review",", vol. 88, no. 2, pp. 135–170, 1981. DOI: ",[393,699,704],{"href":700,"target":655,"className":701},"https:\u002F\u002Fdoi.org\u002F10.1037\u002F0033-295X.88.2.135",[702,703],"text-blue-600","underline","[Online]",[690,706,708,709,712,713],{"id":707},"source-2","T. B. Brown et al., \"Language Models are Few-Shot Learners,\" in ",[536,710,711],{},"Advances in Neural Information Processing Systems",", vol. 33, 2020, ",[393,714,704],{"href":715,"target":655,"className":716},"https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2020\u002Fhash\u002F1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html",[702,703],[690,718,720,721,723,724],{"id":719},"source-3","L. Ouyang et al., \"Training Language Models to Follow Instructions with Human Feedback,\" in ",[536,722,711],{},", vol. 35, 2022, ",[393,725,704],{"href":726,"target":655,"className":727},"https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper_files\u002Fpaper\u002F2022\u002Fhash\u002Fb1efde53be364a73914f58805a001731-Abstract-Conference.html",[702,703],[690,729,731,732,734,735],{"id":730},"source-4","D. Guo et al., \"DeepSeek-R1 Incentivizes Reasoning in LLMs Through Reinforcement Learning,\" ",[536,733,538],{},", vol. 645, pp. 633–638, 2025. DOI: ",[393,736,704],{"href":737,"target":655,"className":738},"https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41586-025-09422-z",[702,703],[690,740,742,743,746,747],{"id":741},"source-5","B. Ichter et al., \"Do As I Can, Not As I Say: Grounding Language in Robotic Affordances,\" in ",[536,744,745],{},"Proc. 6th Conference on Robot Learning (CoRL'22)",", PMLR vol. 205, pp. 287–318, 2023, ",[393,748,704],{"href":749,"target":655,"className":750},"https:\u002F\u002Fproceedings.mlr.press\u002Fv205\u002Fichter23a.html",[702,703],[690,752,754,755,758,759],{"id":753},"source-6","Y. J. Ma et al., \"Eureka: Human-Level Reward Design via Coding Large Language Models,\" in ",[536,756,757],{},"Proc. International Conference on Learning Representations (ICLR'24)",", 2024, ",[393,760,704],{"href":761,"target":655,"className":762},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2310.12931",[702,703],[690,764,766,767,770,771],{"id":765},"source-7","T. Chu et al., \"SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training,\" in ",[536,768,769],{},"Proc. 42nd International Conference on Machine Learning (ICML'25)",", PMLR vol. 267, 2025, ",[393,772,704],{"href":773,"target":655,"className":774},"https:\u002F\u002Fproceedings.mlr.press\u002Fv267\u002Fchu25c.html",[702,703],[690,776,778,779,782,783],{"id":777},"source-8","I. Shenfeld et al., \"RL's Razor: Why Online Reinforcement Learning Forgets Less,\" ",[536,780,781],{},"arXiv",", 2025, ",[393,784,704],{"href":785,"target":655,"className":786},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2509.04259",[702,703],[690,788,790,791,793,794],{"id":789},"source-9","Y. Yue et al., \"Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?,\" in ",[536,792,711],{},", vol. 38, 2025, ",[393,795,704],{"href":796,"target":655,"className":797},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2504.13837",[702,703],[690,799,801,802,804,805],{"id":800},"source-10","S. Yuan et al., \"Understanding Diversity Collapse in RLVR via the Lens of Overtraining,\" ",[536,803,781],{},", 2026, ",[393,806,704],{"href":807,"target":655,"className":808},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.15455",[702,703],[690,810,812,813,816,817],{"id":811},"source-11","P. Villalobos et al., \"Position: Will We Run Out of Data? Limits of LLM Scaling Based on Human-Generated Data,\" in ",[536,814,815],{},"Proc. 41st International Conference on Machine Learning (ICML'24)",", PMLR vol. 235, 2024, ",[393,818,704],{"href":819,"target":655,"className":820},"https:\u002F\u002Fproceedings.mlr.press\u002Fv235\u002Fvillalobos24a.html",[702,703],[690,822,824,825,828,829],{"id":823},"source-12","D. Silver and R. S. Sutton, \"Welcome to the Era of Experience,\" preprint of a chapter in ",[536,826,827],{},"Designing an Intelligence",", MIT Press, 2025, ",[393,830,704],{"href":653,"target":655,"className":831},[702,703],{"title":833,"searchDepth":834,"depth":834,"links":835},"",2,[836,837,838,839,840,844,845],{"id":363,"depth":834,"text":364},{"id":384,"depth":834,"text":385},{"id":505,"depth":834,"text":506},{"id":526,"depth":834,"text":527},{"id":569,"depth":834,"text":570,"children":841},[842],{"id":605,"depth":843,"text":606},3,{"id":629,"depth":834,"text":630},{"id":676,"depth":834,"text":680},"2026-10-20","Language models learn by predicting human text, while reinforcement learning learns from the outcomes of its own attempts. A look at how the two methods differ, what each struggles with on its own, and why chat assistants, reasoning models and robots increasingly rely on both.","md",{"src":850},"https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1715485717725-01a1b81528f5?w=1200&h=750&fit=crop&auto=format",{"authors":852,"badge":858,"source":860},[853],{"avatar":854,"name":856,"to":857},{"src":855},"\u002Fimg\u002Fmark_avatar.png","Mark Williams","#",{"label":859},"Reinforcement Learning",{"name":861,"url":862},"Thinkata Research","https:\u002F\u002Fthinkata.com",true,{"title":230,"description":847},"fXiDA8SJAaDJ2rxywcBHv3K9KEZt_Yq5AWkhVF6SHBw",[867,868],null,{"id":869,"title":226,"body":870,"date":1144,"description":1145,"extension":848,"image":1146,"meta":1147,"navigation":863,"path":227,"seo":1154,"stem":228,"__hash__":1155,"_path":227},"insights\u002Fnews\u002Finsights\u002Fmachine-unlearning-removal-suppression.md",{"type":344,"value":871,"toc":1132},[872,884,890,893,896,909,913,921,924,927,931,939,947,951,954,966,974,978,996,1004,1007,1011,1018,1021,1024,1028,1038,1041,1044],[347,873,351,875,351,879],{"className":874},[350],[353,876,226],{"className":877,"id":878},[356],"forgetting-on-request",[359,880,883],{"className":881,"id":882},[362],"why-removing-data-from-a-trained-model-is-not-like-deleting-a-row","Why Removing Data From a Trained Model Is Not Like Deleting a Row",[347,885,368,886],{"style":367},[370,887],{"src":888,"alt":889,"style":374},"https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1747147292047-7c61f1110897?w=1200&h=750&fit=crop&auto=format","A hand pulling a single index card from a library card catalog drawer",[376,891,892],{},"A deletion request is easy to honor in a database. Find the record, remove it, and look again to confirm it is gone. The same request aimed at a trained language model has nothing comparable to find. Whatever the model absorbed during training is spread across billions of weights, the numerical values it adjusted while learning, and blended with everything else it read.",[376,894,895],{},"A library card catalog is a closer picture of the problem. Pulling a card from the drawer makes a book hard to find, but the book is still on the shelf, and anyone who walks the stacks can come across it. Research from the past two years suggests that the methods built to make models forget mostly pull the card. The model stops giving the answer when asked, while the knowledge behind that answer stays in the weights. Telling those two outcomes apart has turned out to be harder than producing a model that stays quiet.",[376,897,898,899,903,904,908],{},"Machine unlearning is the name for methods that try to remove the influence of particular training data from a model after training has finished. Interest has grown with the European right to be forgotten, with copyright disputes over training data, and with worries about models that know too much about weapons ",[390,900,901],{},[393,902,396],{"href":395},". The cleanest answer is to retrain from scratch without the offending data, and for the largest models that is usually far too expensive. An early design called SISA planned for deletion in advance, splitting the training data into separate shards that each feed a smaller model, so a request only forces a retrain of the shard that held the record ",[390,905,906],{},[393,907,420],{"href":419},". That works when it is chosen before training. For a language model already trained on a large slice of the web, it is too late, which is why cheaper after-the-fact methods exist at all.",[359,910,912],{"id":911},"two-goals-sharing-one-word","Two Goals Sharing One Word",[376,914,915,916,920],{},"A position paper presented at NeurIPS 2025 argues that the word unlearning now covers two different aims ",[390,917,918],{},[393,919,396],{"href":395},". Removal means taking a specific piece of training data out of the model's weights, which in library terms means taking the book off the shelf. Suppression means stopping a kind of content from appearing in the model's answers, which is closer to pulling the card.",[376,922,923],{},"The two come apart in both directions. Removal works on discrete items, a particular article or photograph, so it cannot target a skill the model pieced together from many sources, such as a general recipe for a toxic compound. Succeeding at removal also does not settle what the model can produce. The authors point out that a model trained without a single copyrighted image of a comic book character can still draw something that looks like that character, because generalizing beyond its examples is what the model was built to do.",[376,925,926],{},"Seen this way, a method that makes a model go quiet is doing suppression, whatever its paper calls it. That raises a practical question about how well the quiet holds once someone goes looking.",[359,928,930],{"id":929},"walking-the-stacks","Walking the Stacks",[376,932,933,934,938],{},"A team from Carnegie Mellon University tested exactly that by fine-tuning unlearned models afterward, giving them a short round of extra training on ordinary public text that never contained the forgotten material ",[390,935,936],{},[393,937,515],{"href":514},". A model made to forget the Harry Potter novels was trained on general facts about the characters, written by another model and containing no passages from the books, and it began reproducing text from the novels word for word. Public medical articles brought back hazardous knowledge about bioweapons in the same way. Training on gibberish brought back nothing, which suggests the related text was jogging something still stored rather than teaching it fresh. Stronger unlearning made recovery harder, so the effect is a matter of degree, but the authors' own reading is that current methods hide answers rather than remove knowledge.",[376,940,941,942,946],{},"A second test makes the same point more directly ",[390,943,944],{},[393,945,545],{"href":544},". An attacker is handed some of the facts a model was supposed to forget and fine-tunes on them, then checks whether other facts from the same set come back, ones that could not be guessed from the facts provided. On knowledge the model picked up in its original training, this recovered 88 percent of the accuracy the model had before unlearning. That work is a preprint and has not been peer reviewed, though it lines up closely with the Carnegie Mellon result. In library terms, a visitor who knows roughly which aisle to search finds most of the books whose cards were pulled.",[359,948,950],{"id":949},"checking-the-shelves-not-the-catalog","Checking the Shelves, Not the Catalog",[376,952,953],{},"If a quiet model can still hold the answer, then asking the model and reading its reply is a weak test. A model that says it does not know might have forgotten, or might simply be declining.",[376,955,956,957,961,962,421],{},"One way around this is to invent the data, so the true answer is known in advance. TOFU, introduced at the Conference on Language Modeling in 2024, generated 200 profiles of authors who do not exist, trained a model on them, and asked unlearning methods to make it forget some ",[390,958,959],{},[393,960,555],{"href":554},". Anything the model knows about those authors came from a known source, so an unlearned model can be compared with one that never saw them. None of the methods tested matched that standard. The adversarial study above adds a caution, that facts added in a late round of training, as these fictitious authors are, may be easier to forget than knowledge absorbed from the start, so tests like this one may flatter the methods ",[390,963,964],{},[393,965,545],{"href":544},[376,967,968,969,973],{},"A NeurIPS 2025 benchmark turned the same idea on the measuring tools themselves ",[390,970,971],{},[393,972,565],{"href":564},". Its authors released several hundred models whose true knowledge is known by construction, then asked whether each of a dozen common scores could tell a model that holds a fact from one that does not, and whether that verdict survived routine changes such as compressing the model or briefly exposing it to related text. No score did well on both counts. A widely used score based on word overlap separated knowing from not knowing reasonably well, yet came close to a coin flip once related text was used to jog the model. A score that weakens under exactly the conditions an attacker would create is a shaky basis for claiming that data was deleted.",[359,975,977],{"id":976},"when-the-same-fact-sits-in-several-books","When the Same Fact Sits in Several Books",[347,979,368,980,368,984,368,988],{"style":596},[370,981],{"src":982,"alt":983,"style":374},"https:\u002F\u002Fimages.unsplash.com\u002Fphoto-1573812195421-50a396d17893?w=1200&auto=format&fit=crop","A tightly stacked pile of newspapers seen edge on",[602,985,987],{"style":604,"id":986},"the-same-story-filed-five-ways","The Same Story, Filed Five Ways",[376,989,990,991,995],{"style":609},"Pull one paper out of the stack and the earthquake it reported is still in the four below it, told in different words. Benchmarks have generally treated the data to forget and the data to keep as separate piles, which is not how real collections of text are assembled ",[390,992,993],{},[393,994,579],{"href":578},". A news article covered by a deletion request may state a fact that an encyclopedia entry the model is allowed to keep also states. A model retrained without the article would still learn that fact from the encyclopedia, so an honest unlearning method should leave the fact alone and remove only what was unique to the article.",[376,997,998,999,1003],{},"The DUSK benchmark, published in the Findings of ACL 2026, builds that situation on purpose ",[390,1000,1001],{},[393,1002,579],{"href":578},". Its authors wrote profiles of 120 fictitious professors into five documents in five different styles, with half the profiles appearing in all five documents and the rest in only one, then asked recent unlearning methods to remove a single document.",[376,1005,1006],{},"The methods tended to miss in both directions at once. The wording of the deleted document usually disappeared, yet facts unique to it often survived, while shared facts that should have been kept were damaged. Giving more weight to protecting the kept documents helped at first and then stopped helping. Removing a document and removing the facts it contains are different operations, and current methods have trouble aiming at one without hitting the other.",[359,1008,1010],{"id":1009},"pulling-the-card-without-reading-it-aloud","Pulling the Card Without Reading It Aloud",[376,1012,1013,1014,421],{},"Standard methods also have an awkward habit. To teach a model not to give an answer, they put that answer into the training data and push its likelihood down, so the sensitive text gets handled again in order to be forgotten ",[390,1015,1016],{},[393,1017,589],{"href":588},[376,1019,1020],{},"Researchers at the Technical University of Munich and Mila, the Quebec AI institute, avoid using the target answer at all. Their method has the model write several of its own responses to a question it should forget, keeps the one a scoring rule judges furthest from the original answer, and trains on that. Repeating the loop deliberately sets off the kind of collapse people worry about when models train on their own output, a gradual loss of the original information, but aimed only at the material to be forgotten. The reported result is that the quiet holds up better against two attacks that break other methods, asking the same question many times and forcing the model to continue from the opening words of an answer.",[376,1022,1023],{},"The authors also found that target-based methods leave a fingerprint. They push correct answers so far down that an attacker can find a forgotten fact by looking for the option the model rates least likely, a little like an empty slot in the catalog drawer showing exactly where a card used to be. The work appeared at ICLR 2026, and its claims concern what the model says rather than what its weights hold. It makes the quiet harder to break, which is useful, while leaving open whether the book has left the shelf.",[359,1025,1027],{"id":1026},"what-a-deletion-claim-can-honestly-say","What a Deletion Claim Can Honestly Say",[376,1029,1030,1031,1034,1035,421],{},"The evidence points toward a narrower kind of claim. From a systems perspective, an unlearning method is best treated as an output control, a filter on what the model says, together with a stated list of the attacks it was tested against. A method that holds under direct questioning may fail under relearning or repeated sampling, so a claim that names only the easy test says little. Evaluations whose own scores have been stress tested deserve more weight than ones that simply ask the model. Where genuine removal is required, the options are still the expensive ones, filtering data before training or retraining without it, and that cost is a reason to decide carefully what enters a training set in the first place. Related questions about what a system keeps come up in earlier Thinkata pieces on ",[393,1032,1033],{"href":243},"isolation between customers"," and on ",[393,1036,1037],{"href":291},"agents that rewrite their own memory",[376,1039,1040],{},"The open question is the one the benchmarks keep circling. What evidence would show that a fact is gone from the weights rather than merely hard to reach? Relearning attacks are the closest thing to such a test, and they only set a floor, since a stronger attacker might recover more. Legal practitioners would know better than a systems reading can whether an assurance that depends on which attacks were tried could ever satisfy a deletion obligation.",[376,1042,1043],{},"Until that kind of evidence exists, the catalog picture is probably the fairest description of what unlearning delivers today. The card has been pulled, and newer methods are getting better at keeping it pulled when someone asks directly or tries a few tricks. Whether the book has left the shelf is a separate question, and a deletion record that keeps the two apart would tell both the person who asked for the deletion and the engineers who maintain the model what was actually done.",[347,1045,351,1047,351,1049],{"className":1046},[676,677],[359,1048,680],{"id":676},[682,1050,368,1052,368,1061,368,1072,368,1082,368,1091,368,1101,368,1111,368,1122,351],{"className":1051},[685,686,687,688],[690,1053,1054,1055,793,1057],{"id":692},"A. F. Cooper et al., \"Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research,\" in ",[536,1056,711],{},[393,1058,704],{"href":1059,"target":655,"className":1060},"https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper_files\u002Fpaper\u002F2025\u002Fhash\u002F8110ef9878e4642ad848ca583d37d7ca-Abstract-Position_Paper_Track.html",[702,703],[690,1062,1063,1064,1067,1068],{"id":707},"L. Bourtoule et al., \"Machine Unlearning,\" in ",[536,1065,1066],{},"Proc. 2021 IEEE Symposium on Security and Privacy (SP)",", 2021, pp. 141–159. DOI: ",[393,1069,704],{"href":1070,"target":655,"className":1071},"https:\u002F\u002Fdoi.org\u002F10.1109\u002FSP40001.2021.00019",[702,703],[690,1073,1074,1075,782,1078],{"id":719},"S. Hu, Y. Fu, Z. S. Wu, and V. Smith, \"Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning,\" in ",[536,1076,1077],{},"Proc. International Conference on Learning Representations (ICLR'25)",[393,1079,704],{"href":1080,"target":655,"className":1081},"https:\u002F\u002Fproceedings.iclr.cc\u002Fpaper_files\u002Fpaper\u002F2025\u002Fhash\u002F18fd48d9cbbf9a20e434c9d3db6973c5-Abstract-Conference.html",[702,703],[690,1083,1084,1085,758,1087],{"id":730},"A. Deeb and F. Roger, \"Do Unlearning Methods Remove Information from Language Model Weights?,\" ",[536,1086,781],{},[393,1088,704],{"href":1089,"target":655,"className":1090},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2410.08827",[702,703],[690,1092,1093,1094,758,1097],{"id":741},"P. Maini et al., \"TOFU: A Task of Fictitious Unlearning for LLMs,\" in ",[536,1095,1096],{},"Proc. 1st Conference on Language Modeling (COLM)",[393,1098,704],{"href":1099,"target":655,"className":1100},"https:\u002F\u002Fopenreview.net\u002Fpdf?id=B41hNBoWLo",[702,703],[690,1102,1103,1104,793,1107],{"id":753},"V. Dorna et al., \"OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics,\" in ",[536,1105,1106],{},"Advances in Neural Information Processing Systems, Datasets and Benchmarks Track",[393,1108,704],{"href":1109,"target":655,"className":1110},"https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper_files\u002Fpaper\u002F2025\u002Fhash\u002F3e4a38f228427ab819ba7899003a44b1-Abstract-Datasets_and_Benchmarks_Track.html",[702,703],[690,1112,1113,1114,1117,1118],{"id":765},"W. Jeung et al., \"DUSK: Do Not Unlearn Shared Knowledge,\" in ",[536,1115,1116],{},"Findings of the Association for Computational Linguistics: ACL 2026",", 2026, pp. 42013–42031, ",[393,1119,704],{"href":1120,"target":655,"className":1121},"https:\u002F\u002Faclanthology.org\u002F2026.findings-acl.2085\u002F",[702,703],[690,1123,1124,1125,804,1128],{"id":777},"Y. Scholten, S. Xhonneux, L. Schwinn, and S. Günnemann, \"Model Collapse Is Not a Bug but a Feature in Machine Unlearning for LLMs,\" in ",[536,1126,1127],{},"Proc. International Conference on Learning Representations (ICLR'26)",[393,1129,704],{"href":1130,"target":655,"className":1131},"https:\u002F\u002Fproceedings.iclr.cc\u002Fpaper_files\u002Fpaper\u002F2026\u002Fhash\u002F263da600dcd225dcb9ecaef8290e2012-Abstract-Conference.html",[702,703],{"title":833,"searchDepth":834,"depth":834,"links":1133},[1134,1135,1136,1137,1138,1141,1142,1143],{"id":882,"depth":834,"text":883},{"id":911,"depth":834,"text":912},{"id":929,"depth":834,"text":930},{"id":949,"depth":834,"text":950},{"id":976,"depth":834,"text":977,"children":1139},[1140],{"id":986,"depth":843,"text":987},{"id":1009,"depth":834,"text":1010},{"id":1026,"depth":834,"text":1027},{"id":676,"depth":834,"text":680},"2026-10-13","A deletion request aimed at a trained language model has no row to remove. Evidence from relearning attacks, benchmarks built on invented data, and documents that share facts suggests that current unlearning methods mostly make a model quieter rather than emptier, and that telling quiet from gone is the harder problem.",{"src":888},{"authors":1148,"badge":1151,"source":1153},[1149],{"avatar":1150,"name":856,"to":857},{"src":855},{"label":1152},"Machine Unlearning",{"name":861,"url":862},{"title":226,"description":1145},"M6mQ-UU0dsXGw1N2TcuejIEgUKVuxtORzEz9y-A4KYc",1792582875758]