gpt-5.6-sol

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range n avgch eod p pre techQ news prose
0 23928 23928 3092 0.0 0.04 0.0 4.17 20.39 59.21
23928 45000 21072 3081 50.02 0.02 0.0 4.1 19.51 58.45
45000 95000 50000 3075 73.56 0.03 0.0 4.0 19.81 59.22
95000 110000 15000 3138 86.37 0.01 0.0 4.17 19.97 59.13
110000 145000 35000 5780 87.44 0.07 0.0 11.53 11.41 15.35
145000 182016 37016 6173 93.62 0.11 0.0 12.93 9.84 8.49
(1009, 1157, "'4 hours ago\\nMonday, November 05, 2007\\nSince tonight is Guy Fawkes Night in England I have been thinking about torture. Guy Fawkes, after being captured near barrels of gunpowder in the basement of the British Parliament, was executed for treason using a rather barbaric, by modern standards, method o'")
(1010, 2848, '"Set up timesheets\\n|Before your team members can start Step 2: Turn in a timesheet, an administrator needs to set up a few things. Some things are required, and other options are there in case you want to go beyond the basics.|\\nThe easiest way to set things up\\nIf you don\'t want to do a whole lot of w"')
(2018, 575, "'[image: Baby bulb]\\nUse this blog to:\\n- Ask Professor Plant any questions you may have.\\n- Share interesting stories or photographs with other schools.\\n- Once you send a message, Professor Plant will read it then publish it onto the website.\\nTo be web safe:\\n- We will not publish any childrens names.\\n-'")
(2019, 1613, "'Relocated NY 298\\nIn the early 1970s, and also again in the mid-70s, there was a proposal for a 4-lane at-grade expressway relocation of NY 298 running from Carrier Circle to NY 31 near Bridgeport. This expressway would have been an extension of the then-proposed North-South Arterial (a precursor to '")
(3027, 2192, "'horizon hotel group\\nTwo Roads Hospitality Assumes Management of The Elms Hotel & Spa in Excelsior Springs, Missouri as Part of Destination Hotels Brand\\nDestination Hotels | April 30, 2018\\nDENVER, CO. – April 30, 2018 - Two Roads Hospitality, the international lifestyle company featuring an unrivaled'")
(3028, 3942, '"BOOKNMEET Oral And Maxillofacial Surgeon FAQ\\nHOW DO I FIND BEST DENTAL SPECIALIST DOCTOR IN Ernakulam ?\\nIndia\'s best Online appointment platform helps finding a specialist doctor so easy for everyone. Simple categories such as Specialty, Clinic name, Hospital name and Name of the consultant search i"')
(4036, 493, "'Stevie Wonder and House Of Blues Sunday 5th June 2011 9th Annual GLAD Benefit Extravaganza held at The House of Blues - Show Los Angeles, California\\n« PREV · House Of Blues Gallery · NEXT »\\nAll photos and pictures on Contactmusic.com are subject to copyright. At no time should photographs be taken f'")
(4037, 247, "'The last time we saw this kind of wonderfully evil trickery played out on unsuspecting citizens, it was a clever advertisement for a movie. But we didn’t care. It was deliciously wrong, and we loved it.\\nEnter devil baby. Same thing.\\nEnjoy. We did.'")
(5045, 1462, "'For years, black and silver were always at the top of most desired colors for cars. But as of 2011, white has become the predominant color choice among American buyers. A survey published last year says that white made up 20 percent of the 2011-model-year cars.\\nIn an interview with Yahoo Autos, Sand'")
(5046, 4513, "'Superman Homepage writer Michael J. Petty reviews episodes from the “Krypton” TV series, airing on SYFY.\\nCheck out his review of the 7th episode of Season 2 in which General Zod pushes for control of a dominating weapon; Seg and Nyssa fight to save Seg’s life.\\nWRITTEN BY: Nadria Tucker\\nDIRECTED BY: '")
(6054, 2586, "'CSIA: next-term system integrator leaders, 12% membership gain\\nControl System Integrators Association announced new leadership at its 2011 annual conference in April and noted a 12% gain in CSIA membership during the past year. Stephen M. Goldberg was elected to a three-year term as chairman. Link t'")
(6055, 2003, "'Case Against Kopp Accomplices to Move to Different Court\\nFederal charges against anti-abortion extremists Loretta Marra and Dennis Malvasi were dropped in Buffalo on Friday by District Judge Richard Arcarra, clearing the way for the trial to be moved to a federal court in New York City, according to'")
(7063, 4150, "'Em artigo publicado em The Economist, Samuel Johnson trata sobre o novo livro de Antonin Scalia, juiz da Suprema Corte Americana, e Bryan Garner, lexicógrafo, chamado: “Reading Law: The Interpretation of Legal Texts”.\\nSegundo os autores, os juízes isolados de reprovação popular, sentem-se atraídos p'")
(7064, 1955, "'he boxes are a specific kind of containers used to stock products safely. The most popular type of boxes are of cardboard packaging. There are various types of custom boxes available for particular purposes. The most applicable category of custom boxes is tote boxes. Tote Boxes are the particular ty'")
(8072, 1385, "'June 28 (Reuters) - Boeing Co said on Wednesday its Chief Financial Officer Greg Smith will take on additional roles, ahead of the planned retirement of some of its key executives later this year.\\nThe range of duties that will shift to Smith includes overseeing the launch of Boeing Global Services o'")
(8073, 3636, "'Residents of Rosettenville who wanted to hold a protest march in the suburb south of Johannesburg were frustrated after police denied them permission to proceed on Saturday. The protesters, who claimed to be mobilising against “drugs, prostitution, property theft and brothels”, were met by a large c'")
(9081, 3040, "'A Beacon Valley orphanage is working with food activists to fight hunger in homes right on its doorstep.\\nBaitul Ansaar Child Care Centre, a non-profit, launched its food garden, which is part of its 100 Home Project, earlier this month after finding that poverty and hunger were stalking families in '")
(9082, 3411, "'Proposed Human Rights Act reforms would fundamentally undermine the rights of humanists\\nYesterday, the Government published its long-awaited proposals on reforming the Human Rights Act. If passed into law, these reforms will have a devastating impact on the fundamental rights of humanists.\\nThe Gover'")
(10090, 963, "'It always seems to be Lucas featuring in my Living Arrows posts but as difficult as it is to catch a photograph of him it is virtually impossible with the twins. Even if I manage to spot them being relatively still if they spot me and the camera they are on the move; I have so many photos of them th'")
(10091, 1549, "'“narrowly tailored to protect a legitimate business interest”\\nQuestion we are often asked:\\nIs your non-compete reasonable and enforceable under Virginia law?\\nAnswer we most often give:\\nYes, if it is narrowly tailored to protect the legitimate business interest of the company…\\nWhat does that really m'")
(11099, 3425, '\'CLAY CITY, Ky. (AP) - Investigators were trying to determine Thursday how a prisoner was able to grab a police chief\\\'s gun and kill him with a point-blank shot to the back of the head, all while handcuffed behind plexiglass in the back seat of a squad car.\\n"That\\\'s kind of a mystery to us," said Powe\'')
(11100, 380, "'Please email me Fast News, and communications, including product and service information and special offers, from Ford Motor Company, and its dealers.\\nOur Privacy Pledge\\nThank you for your interest in Fast News from Ford Motor Company.\\nWin A 2016 Ford Mustang GT In The 5.0 Fever Sweepstakes Presente'")
(12108, 3059, "'What is Business Mobile Banking?\\nBusiness Mobile Banking is our mobile service that brings business banking to your phone. Business Mobile Banking allows you to monitor your account from your phone at any time.\\nWhat can I do with Business Mobile Banking?\\nBusiness Mobile Banking allows you to:\\n- Revi'")
(12109, 337, "'Rockland County, NY\\nBergen County, NJ\\nOutside your front door, your home has two jobs. Most important, it provides shelter for you and your family. But it also serves as your face to the world outside your front door. Trust Xtreme Gutters & Roofing to keep the elements out and the look of your home '")
(13117, 1303, "'Ann Arbor, MI — Powerful magnification and bright lighting from multiple sources distinguish the new Aven Gemscope, made by Carton Optical Co., Ltd. This specialized binocular stereo microscope has a magnification range of 10x to 44x. Upper and lower illumination assures bright, distortion-free surf'")
(13118, 2878, "'Move over, Donald Trump. There’s someone even better at delivering employment-ending news at a moment’s notice with the whole world watching.\\nAmerican Jessica Korda stunningly fired caddie Jason Gilroyed on the ninth hole Saturday, creating a reality television-like sideshow in the third round of th'")
(14126, 1583, "'I was on a marketing course in St. Catherine’s a few weeks ago when we were discussing what makes Lux Furniture Rentals different. The marketing coach then preceded to say “well, you are the most expensive rental house on the market and…”. That’s when I passionately cut her off to explain that was n'")
(14127, 1002, '"Swimming Pool Filter Media Sand / Glass\\nWe\'ve been selling filter media online for many years and here at 1st Direct Pools we want you to have the best advice! There are many different types of filter media, depending on the application. However, the popular media is the new recycled glass instead o"')
(15135, 494, "'Borbonese medium luna grey bag\\nRegular Price: €215.00\\nSpecial Price €129.00\\nBorbonese medium luna grey bag, in grey jet fabric, leather borders, ribbon with logo, golden hardware. Fabric lining with pcoket and zip, double opened pocket.\\n- adjustable shoulder strap with logo\\n- zip fastening\\n- Dimensi'")
(15136, 1171, '"Runetki- website which is intended primarily for Russian-speaking users but it is not important because there are many girls who speak foreign languages! If you are lonely, not enough female warmth and attention, on this site you can it\'s nice to talk to girls and satisfy their sexual needs, stay al"')

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  243       06:41  0.0 125868 node /usr/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model azure_ai/gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=high -c model_reasoning_effort=xhigh -c request_max_retries=12 -c stream_max_retries=12 -c stream_idle_timeout_ms=600000 -- # Task: Curate a raw web corpus to train the best small language model  ## Context You are assembling the pretraining data for a small language model. You have a large pool of **raw web documents** at `/workspace/data/pool.jsonl` — one JSON object per line: `{"id": <int>, "text": "..."}`. The documents are unlabeled. The pool holds **182,016 documents (~200M GPT-2 tokens total)**.  Compute is fixed: the model is trained on a **fixed budget of 12,000,000 tokens** — only ~6% of the pool, far smaller than the whole thing. So you cannot train on everything: you must decide *which* documents are worth training on. The training pipeline tokenizes your selection (GPT-2 BPE) in priority order and stops once it has accumulated 12M tokens.  ## Goal Curate the pool: choose the documents that, trained on under the fixed budget, produce the **best language model**. Quality is measured by **held-out perplexity on a BROAD, multi-domain high-quality English target** — equal parts encyclopedic (Wikipedia), general high-quality web prose, news, and technical Q&A. This is your disclosed target: select data that makes the model good across *all* of these registers, not just one. Lower perplexity is better.  ## Deliverables 1. `/workspace/submission/selection.json` — a JSON list of pool `id`s to train    on, in **priority order** (best first). The training pipeline consumes your    list in order until the **12M-token** budget is filled, then trains. Provide    enough ids to cover at least 12M tokens (a few hundred MB of text is ample);    ids beyond the budget are simply unused. 2. `/workspace/submission/curate.py` — the reproducible script that produced    your selection from a stated criterion (not a hand-picked id list). 3. `/workspace/claim.md` — Hypothesis / Mechanism (predict an observable other    than the final perplexity) / Falsification / Transfer.  ## Resources - The **frozen training script** is provided (`/workspace/train_nano.py`, with   `model.py`). It trains a ~30M-parameter GPT from scratch on a token budget with   fixed hyperparameters. **You may run it to evaluate a candidate selection**   against a dev target (`/workspace/data/multi_dev.npy`) — one run is ~60s on the   GPU — but you **cannot change it**; the official run uses an identical frozen   copy. The only thing you control is the data selection. - Standard GPU sandbox (Python, PyTorch, transformers). How you assess document   quality is entirely up to you (filters, classifiers, heuristics, a model, etc.). - Your session ends when the wall-clock budget runs out — check   `/workspace/.timer/remaining_secs` at any point for the authoritative time left   (don't assume a fixed number of hours). Every training run you do comes out of   that budget. - No internet. The pool's documents are freshly assembled; you cannot look up   any quality labels — any quality signal must come from your own analysis.  ## How you are scored Official: your selection fills the fixed token budget, the frozen script trains the 30M GPT from scratch, and the verifier measures the **absolute held-out perplexity on the hidden high-quality target** (a different sample from the disclosed domain than the dev set). That perplexity is your score. **Lower is better** — the goal is simply to push held-out perplexity as low as you can.  The verifier reports the raw perplexity and does no normalization. For reference and for a separate downstream normalization step, the benchmark records two fixed anchors: a **baseline** of held-out perplexity from a random selection (the do-nothing reference), and a **theoretical best** of perplexity 1 (the perplexity floor, unreachable in practice). Aim to get your held-out perplexity well below the random baseline.  ## Constraints - `selection.json` must be produced by `curate.py` from a stated criterion; an   opaque hand-picked id list fails the publishability check even if it scores well. - A selection with duplicate or out-of-range ids, or too few tokens to fill the   budget, is marked invalid (no score recorded) and excluded from ranking, so it   can't win and doesn't distort averages. 
  251       06:41  0.8 359824 /usr/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model azure_ai/gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=high -c model_reasoning_effort=xhigh -c request_max_retries=12 -c stream_max_retries=12 -c stream_idle_timeout_ms=600000 -- # Task: Curate a raw web corpus to train the best small language model  ## Context You are assembling the pretraining data for a small language model. You have a large pool of **raw web documents** at `/workspace/data/pool.jsonl` — one JSON object per line: `{"id": <int>, "text": "..."}`. The documents are unlabeled. The pool holds **182,016 documents (~200M GPT-2 tokens total)**.  Compute is fixed: the model is trained on a **fixed budget of 12,000,000 tokens** — only ~6% of the pool, far smaller than the whole thing. So you cannot train on everything: you must decide *which* documents are worth training on. The training pipeline tokenizes your selection (GPT-2 BPE) in priority order and stops once it has accumulated 12M tokens.  ## Goal Curate the pool: choose the documents that, trained on under the fixed budget, produce the **best language model**. Quality is measured by **held-out perplexity on a BROAD, multi-domain high-quality English target** — equal parts encyclopedic (Wikipedia), general high-quality web prose, news, and technical Q&A. This is your disclosed target: select data that makes the model good across *all* of these registers, not just one. Lower perplexity is better.  ## Deliverables 1. `/workspace/submission/selection.json` — a JSON list of pool `id`s to train    on, in **priority order** (best first). The training pipeline consumes your    list in order until the **12M-token** budget is filled, then trains. Provide    enough ids to cover at least 12M tokens (a few hundred MB of text is ample);    ids beyond the budget are simply unused. 2. `/workspace/submission/curate.py` — the reproducible script that produced    your selection from a stated criterion (not a hand-picked id list). 3. `/workspace/claim.md` — Hypothesis / Mechanism (predict an observable other    than the final perplexity) / Falsification / Transfer.  ## Resources - The **frozen training script** is provided (`/workspace/train_nano.py`, with   `model.py`). It trains a ~30M-parameter GPT from scratch on a token budget with   fixed hyperparameters. **You may run it to evaluate a candidate selection**   against a dev target (`/workspace/data/multi_dev.npy`) — one run is ~60s on the   GPU — but you **cannot change it**; the official run uses an identical frozen   copy. The only thing you control is the data selection. - Standard GPU sandbox (Python, PyTorch, transformers). How you assess document   quality is entirely up to you (filters, classifiers, heuristics, a model, etc.). - Your session ends when the wall-clock budget runs out — check   `/workspace/.timer/remaining_secs` at any point for the authoritative time left   (don't assume a fixed number of hours). Every training run you do comes out of   that budget. - No internet. The pool's documents are freshly assembled; you cannot look up   any quality labels — any quality signal must come from your own analysis.  ## How you are scored Official: your selection fills the fixed token budget, the frozen script trains the 30M GPT from scratch, and the verifier measures the **absolute held-out perplexity on the hidden high-quality target** (a different sample from the disclosed domain than the dev set). That perplexity is your score. **Lower is better** — the goal is simply to push held-out perplexity as low as you can.  The verifier reports the raw perplexity and does no normalization. For reference and for a separate downstream normalization step, the benchmark records two fixed anchors: a **baseline** of held-out perplexity from a random selection (the do-nothing reference), and a **theoretical best** of perplexity 1 (the perplexity floor, unreachable in practice). Aim to get your held-out perplexity well below the random baseline.  ## Constraints - `selection.json` must be produced by `curate.py` from a stated criterion; an   opaque hand-picked id list fails the publishability check even if it scores well. - A selection with duplicate or out-of-range ids, or too few tokens to fill the   budget, is marked invalid (no score recorded) and excluded from ranking, so it   can't win and doesn't distort averages. 
 1056       00:35  0.0  5920 /bin/bash -c python submission/curate.py --output /tmp/balanced_v1.json && python - <<'PY' import json x=json.load(open('/tmp/balanced_v1.json')); print(len(x),len(set(x)),min(x),max(x),x[:30]) PY bash dev_eval.sh /tmp/balanced_v1.json
 1057       00:35 96.9 40580 python submission/curate.py --output /tmp/balanced_v1.json
4959

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{"chunk_id":"6b6a13","wall_time_seconds":30.001848411,"session_id":64176,"original_token_count":171,"output":"wrote 182016 unique ids to /tmp/balanced_v1.json\nestimated prefix tokens by register: {'encyclopedic': 3500753, 'web': 3499690, 'news': 3500332, 'technical': 3499829}\npool documents by assigned register: {'encyclopedic': 7922, 'web': 157699, 'news': 4868, 'technical': 11527}\n182016 182016 0 182015 [9117, 10065, 12258, 1029, 19292, 39891, 14266, 12644, 11571, 830, 335, 3625, 30688, 8000, 37981, 10679, 30168, 25901, 46027, 43088, 4148, 19922, 12099, 90515, 11376, 40136, 8987, 14413, 41846, 21846]\nToken indices sequence length is longer than the specified maximum sequence length for this model (2032 > 1024). Running this sequence through the model will result in indexing errors\n"}

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{"chunk_id":"dea6f6","wall_time_seconds":5.001684269,"session_id":64176,"original_token_count":16,"output":"packed 12000000 tokens from 182016 ids -> /tmp/dev_train.npy\n"}

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  9117 encyclopedic   3.42  8799 'Nha Trang - Nha Trang is a popular seaside resort known for its palm-lined white sand beaches and clear turquoise water. Snorkeling and scub'
 10065 web            3.00  2855 'The images, text and information by laura sweet disponível this sítio are licensed and protected under a Creative Commons Attribution-Noncom'
 12258 news           3.67  4760 'JNN 07 Jan 2015 LAHORE: A PML-N Punjab Assembly member has revealed that a multinational food chain, McDonalds, imported 71 tonnes of substa'
  1029 technical      3.77 10920 'View Article in PDF THE term “hacker” typically conjures up images of computer-savvy villains devilishly clicking and typing their way into '
 19292 web            3.00  1986 "INTRODUCING: Joshua With New EP 'Club Nirvana' OFFICIAL PRESS RELEASE NEWS PROVIDED BY Sensational Pop Artist ‘JOSHUA’ is back with his beau"
 39891 news           3.66  6291 "WASHINGTON (AP)—Counterterrorism officials have issued security bulletins to police around the nation about terrorists' desire to attack sta"
 14266 web            3.00  2633 'Massachusetts’ second most populous city, Worcester, announces its partnership with Passport. The City is launching Passport’s mobile paymen'
 12644 web            3.00  2863 'Brucetown Telecom Equipment Dealers Search If you tried to find a Telecom Equipment dealer in Brucetown, probably you have already known tha'
 11571 encyclopedic   3.42  4890 '“What a business is this of a portrait painter! You bring him a potato and expect he will paint you a peach.” - Gilbert Charles Stuart For A'
   830 web            2.99  5497 'Monday morning bus stops will be full of chilly children doing the two-sneaker shuffle dance, hands stuffed in wind breaker pockets, collars'
   335 technical      3.74  4353 'Ozirion is an experimental Web browser allowing people and groups to improve their privacy on the Internet by hiding their IP address throug'
  3625 news           3.65  4053 'The United States claims that Viktor Vekselberg, a billionaire and close ally of Russian President Vladimir Putin, owns the mega-yacht Tango'
 30688 encyclopedic   3.41  5433 'Dove Cameron (born Chloe Celeste Hosterman; January 15, 1996) is an American actress and singer, best known for playing a dual role as both '
  8000 news           3.65 16179 'Democrats and Republicans squabbled over whether they would debate an issue in front of the press -- a squabble that is now being argued in '
 37981 technical      3.72 14184 '- 5 Key Takeaways from the Top Ecommerce Marketplaces’ Ad Strategies - May 11, 2022 - Instapage vs Leadpages vs Unbounce vs Clickfunnels - '
 10679 web            2.99  2739 'Geneva- An Emirati princess, who last year said she was being held "hostage" in a palace, has assured the UN rights chief Michelle Bachelet '
 30168 web            2.99  1853 'Live 2007: 4th Annual Concert Tour SFJAZZ Collective’s new two-disc set, drawn from a recent tour, is likely the last to be led by saxophoni'
 25901 encyclopedic   3.40  7501 'Is Yolanda Adams married? No, she isn’t. She is living a single life with freedom from the worries of the relationship. However, she has oft'
 46027 web            2.99  7664 'Liverpool 2 Ateltico Madrid 1 (agg 2-2, Ateltico go through on away goals): United flop Forlan dashes hopes of an all-English final When the'
 43088 encyclopedic   3.38  6951 'Tommy Hilfiger Estates and Homes ( 3 ) Palm Beach Home Tommy Hilfiger owns a beautiful villa on the very upscale and private island of Musti'
  4148 web            2.99  2421 'Key parts of the infrastructure supporting an espionage campaign that targeted governments around the world reportedly have been shut down i'
 19922 technical      3.70  6991 'Learn how to design databases, secure databases, and keep them in tip-top shape, with SQL Server 2012. Shows how OOP techniques can streamli'
 12099 web            2.99  6009 'The National Retail Federation spent weeks urging the White House to include in the president’s State of the Union address a plug for retail'
 90515 news           3.56  4839 'BUDAPEST (Reuters) - Hungarians handed their maverick Prime Minister Viktor Orban another four years in power, election results showed on Mo'
 11376 encyclopedic   3.37 12926 '|This article needs additional citations for verification. (May 2011) (Learn how and when to remove this template message)| |Founded by||Has'
 40136 news           3.53  4586 "OTTAWA - Foreign Affairs Minister John Baird condemned Syria's fatal shelling of a Turkish border town that left five civilians dead Wednesd"
  8987 technical      3.70  6143 'Our Current Vacancies List At edata4you, we are very clear about our motive and idea of work. We base everything on the requirement of the c'
 14413 web            2.99  5466 'January 24, 2013 2 Comments This is just cool (thanks, Jeff): Politics, parenting, science, education, and pretty much anything I find inter'
 41846 news           3.53  2672 "- Egypt's army chief Gen. Abdel Fattah el-Sisi says he may run for president - But only if the people want him to, he says - A referendum on"
 21846 web            2.99  7076 'At the heart of any great law school are stellar teachers and scholars who readily share their ideas and experiences with students. In the f'
 43302 technical      3.68  8051 'Beating learn-to-program anxiety with good gamification and courses I have anxiety about learning technical skills. I wrote about this a lit'
  4201 news           3.51  4503 'Appeals panel rejects lower court ruling on NJ police force The decision is unlikely to roll back the Metro Division of the Camden County-ru'
110251 encyclopedic   3.36 14055 '|Publication number||US5584138 A| |Application number||US 08/524,765| |Publication date||17 Dec 1996| |Filing date||7 Sep 1995| |Priority da'
 15767 news           3.50  4599 'MOSCOW (AP) — Officials in Kyrgyzstan on Tuesday nullified the results of a weekend parliamentary election after mass protests erupted in th'
 59983 web            2.99  2079 'United is reportedly close to signing Radamel Falcao from Atletico Madrid. The Colombian striker is ready to leave the Spanish club this sum'
 70123 technical      3.67  4804 'Ref #: 27443 Functional Area: Technology Employee Type: Full Time Experienced Required: Please See Below Education Required: Bachelors Degre'
 44899 web            2.99  4372 'Last Updated: 11:20 AM, January 18, 2013 Posted: 10:54 PM, January 17, 2013 PARK CITY, UTAH — The timing couldn’t be more perfect: Just one '
  8365 news           3.49 16460 'Hurricanes, earthquakes, wildfires — the natural disasters that pummeled various parts of the United States and Mexico in 2017 crippled comm'
 83761 technical      3.67 11069 "Sony Xperia P Android phone Sony Xperia P review: WhiteMagic isn't really magic, but the Xperia P is a superbly constructed smartphone let d"
  4577 web            2.99  4277 'We weighed a group of steers and heifers off of ryegrass the other day, and many ranged from 770-850 lbs. That made me think about an old ru'

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  243       09:44  0.0 125868 node /usr/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model azure_ai/gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=high -c model_reasoning_effort=xhigh -c request_max_retries=12 -c stream_max_retries=12 -c stream_idle_timeout_ms=600000 -- # Task: Curate a raw web corpus to train the best small language model  ## Context You are assembling the pretraining data for a small language model. You have a large pool of **raw web documents** at `/workspace/data/pool.jsonl` — one JSON object per line: `{"id": <int>, "text": "..."}`. The documents are unlabeled. The pool holds **182,016 documents (~200M GPT-2 tokens total)**.  Compute is fixed: the model is trained on a **fixed budget of 12,000,000 tokens** — only ~6% of the pool, far smaller than the whole thing. So you cannot train on everything: you must decide *which* documents are worth training on. The training pipeline tokenizes your selection (GPT-2 BPE) in priority order and stops once it has accumulated 12M tokens.  ## Goal Curate the pool: choose the documents that, trained on under the fixed budget, produce the **best language model**. Quality is measured by **held-out perplexity on a BROAD, multi-domain high-quality English target** — equal parts encyclopedic (Wikipedia), general high-quality web prose, news, and technical Q&A. This is your disclosed target: select data that makes the model good across *all* of these registers, not just one. Lower perplexity is better.  ## Deliverables 1. `/workspace/submission/selection.json` — a JSON list of pool `id`s to train    on, in **priority order** (best first). The training pipeline consumes your    list in order until the **12M-token** budget is filled, then trains. Provide    enough ids to cover at least 12M tokens (a few hundred MB of text is ample);    ids beyond the budget are simply unused. 2. `/workspace/submission/curate.py` — the reproducible script that produced    your selection from a stated criterion (not a hand-picked id list). 3. `/workspace/claim.md` — Hypothesis / Mechanism (predict an observable other    than the final perplexity) / Falsification / Transfer.  ## Resources - The **frozen training script** is provided (`/workspace/train_nano.py`, with   `model.py`). It trains a ~30M-parameter GPT from scratch on a token budget with   fixed hyperparameters. **You may run it to evaluate a candidate selection**   against a dev target (`/workspace/data/multi_dev.npy`) — one run is ~60s on the   GPU — but you **cannot change it**; the official run uses an identical frozen   copy. The only thing you control is the data selection. - Standard GPU sandbox (Python, PyTorch, transformers). How you assess document   quality is entirely up to you (filters, classifiers, heuristics, a model, etc.). - Your session ends when the wall-clock budget runs out — check   `/workspace/.timer/remaining_secs` at any point for the authoritative time left   (don't assume a fixed number of hours). Every training run you do comes out of   that budget. - No internet. The pool's documents are freshly assembled; you cannot look up   any quality labels — any quality signal must come from your own analysis.  ## How you are scored Official: your selection fills the fixed token budget, the frozen script trains the 30M GPT from scratch, and the verifier measures the **absolute held-out perplexity on the hidden high-quality target** (a different sample from the disclosed domain than the dev set). That perplexity is your score. **Lower is better** — the goal is simply to push held-out perplexity as low as you can.  The verifier reports the raw perplexity and does no normalization. For reference and for a separate downstream normalization step, the benchmark records two fixed anchors: a **baseline** of held-out perplexity from a random selection (the do-nothing reference), and a **theoretical best** of perplexity 1 (the perplexity floor, unreachable in practice). Aim to get your held-out perplexity well below the random baseline.  ## Constraints - `selection.json` must be produced by `curate.py` from a stated criterion; an   opaque hand-picked id list fails the publishability check even if it scores well. - A selection with duplicate or out-of-range ids, or too few tokens to fill the   budget, is marked invalid (no score recorded) and excluded from ranking, so it   can't win and doesn't distort averages. 
  251       09:44  0.7 360244 /usr/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model azure_ai/gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=high -c model_reasoning_effort=xhigh -c request_max_retries=12 -c stream_max_retries=12 -c stream_idle_timeout_ms=600000 -- # Task: Curate a raw web corpus to train the best small language model  ## Context You are assembling the pretraining data for a small language model. You have a large pool of **raw web documents** at `/workspace/data/pool.jsonl` — one JSON object per line: `{"id": <int>, "text": "..."}`. The documents are unlabeled. The pool holds **182,016 documents (~200M GPT-2 tokens total)**.  Compute is fixed: the model is trained on a **fixed budget of 12,000,000 tokens** — only ~6% of the pool, far smaller than the whole thing. So you cannot train on everything: you must decide *which* documents are worth training on. The training pipeline tokenizes your selection (GPT-2 BPE) in priority order and stops once it has accumulated 12M tokens.  ## Goal Curate the pool: choose the documents that, trained on under the fixed budget, produce the **best language model**. Quality is measured by **held-out perplexity on a BROAD, multi-domain high-quality English target** — equal parts encyclopedic (Wikipedia), general high-quality web prose, news, and technical Q&A. This is your disclosed target: select data that makes the model good across *all* of these registers, not just one. Lower perplexity is better.  ## Deliverables 1. `/workspace/submission/selection.json` — a JSON list of pool `id`s to train    on, in **priority order** (best first). The training pipeline consumes your    list in order until the **12M-token** budget is filled, then trains. Provide    enough ids to cover at least 12M tokens (a few hundred MB of text is ample);    ids beyond the budget are simply unused. 2. `/workspace/submission/curate.py` — the reproducible script that produced    your selection from a stated criterion (not a hand-picked id list). 3. `/workspace/claim.md` — Hypothesis / Mechanism (predict an observable other    than the final perplexity) / Falsification / Transfer.  ## Resources - The **frozen training script** is provided (`/workspace/train_nano.py`, with   `model.py`). It trains a ~30M-parameter GPT from scratch on a token budget with   fixed hyperparameters. **You may run it to evaluate a candidate selection**   against a dev target (`/workspace/data/multi_dev.npy`) — one run is ~60s on the   GPU — but you **cannot change it**; the official run uses an identical frozen   copy. The only thing you control is the data selection. - Standard GPU sandbox (Python, PyTorch, transformers). How you assess document   quality is entirely up to you (filters, classifiers, heuristics, a model, etc.). - Your session ends when the wall-clock budget runs out — check   `/workspace/.timer/remaining_secs` at any point for the authoritative time left   (don't assume a fixed number of hours). Every training run you do comes out of   that budget. - No internet. The pool's documents are freshly assembled; you cannot look up   any quality labels — any quality signal must come from your own analysis.  ## How you are scored Official: your selection fills the fixed token budget, the frozen script trains the 30M GPT from scratch, and the verifier measures the **absolute held-out perplexity on the hidden high-quality target** (a different sample from the disclosed domain than the dev set). That perplexity is your score. **Lower is better** — the goal is simply to push held-out perplexity as low as you can.  The verifier reports the raw perplexity and does no normalization. For reference and for a separate downstream normalization step, the benchmark records two fixed anchors: a **baseline** of held-out perplexity from a random selection (the do-nothing reference), and a **theoretical best** of perplexity 1 (the perplexity floor, unreachable in practice). Aim to get your held-out perplexity well below the random baseline.  ## Constraints - `selection.json` must be produced by `curate.py` from a stated criterion; an   opaque hand-picked id list fails the publishability check even if it scores well. - A selection with duplicate or out-of-range ids, or too few tokens to fill the   budget, is marked invalid (no score recorded) and excluded from ranking, so it   can't win and doesn't distort averages. 
 1358       00:35 98.6 5314744 python3 train_nano.py --train_npy /tmp/dev_train.npy --heldout_npy /workspace/data/multi_dev.npy --out_json /tmp/dev.json --max_iters 3000 --seed 1337
 1390       00:27 97.6 6465600 python pack_selection.py /tmp/early.json /tmp/early.npy
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