gpt-5.6-sol

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Wall time 0.3 seconds
Output:
5390
"""From-scratch nano-GPT (30M) LM training on a 1D token .npy; report held-out perplexity.
Frozen recipe — the ONLY thing that varies between runs is the training token set."""
import argparse, math, time, numpy as np, torch
from model import GPT, GPTConfig

ap = argparse.ArgumentParser()
ap.add_argument("--train_npy", required=True)
ap.add_argument("--heldout_npy", required=True)
ap.add_argument("--out_json", required=True)
ap.add_argument("--max_iters", type=int, default=3000)
ap.add_argument("--warmup", type=int, default=150)
ap.add_argument("--batch", type=int, default=32)
ap.add_argument("--block", type=int, default=256)
ap.add_argument("--lr", type=float, default=6e-4)
ap.add_argument("--seed", type=int, default=1337)
a = ap.parse_args()

torch.manual_seed(a.seed); np.random.seed(a.seed)
dev = "cuda"
tr = np.load(a.train_npy); ho = np.load(a.heldout_npy)
tr = torch.from_numpy(tr.astype(np.int64)); ho = torch.from_numpy(ho.astype(np.int64))
rng = np.random.default_rng(a.seed)

model = GPT(GPTConfig(block_size=a.block, vocab_size=50257, n_layer=6, n_head=6,
                      n_embd=384, dropout=0.0, bias=False)).to(dev)
opt = model.configure_optimizers(0.1, a.lr, (0.9, 0.95), "cuda")

def lr_at(it):
    if it < a.warmup: return a.lr * (it + 1) / (a.warmup + 1)
    r = (it - a.warmup) / max(1, a.max_iters - a.warmup)
    return 0.1 * a.lr + 0.5 * (1 + math.cos(math.pi * r)) * (a.lr - 0.1 * a.lr)

def get_batch(src):
    ix = rng.integers(0, len(src) - a.block - 1, size=a.batch)
    x = torch.stack([src[i:i+a.block] for i in ix]).to(dev)
    y = torch.stack([src[i+1:i+1+a.block] for i in ix]).to(dev)
    return x, y

t0 = time.time()
model.train()
for it in range(a.max_iters):
    for g in opt.param_groups: g["lr"] = lr_at(it)
    x, y = get_batch(tr)
    with torch.autocast("cuda", dtype=torch.bfloat16):
        _, loss = model(x, y)
    opt.zero_grad(set_to_none=True); loss.backward()
    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0); opt.step()
train_s = time.time() - t0

# held-out perplexity: mean next-token CE over non-overlapping windows (window-weighted)
import json
model.eval()
nwin = (len(ho) - 1) // a.block
starts = [j * a.block for j in range(nwin)]
sum_loss, nseen = 0.0, 0
with torch.no_grad():
    for i in range(0, nwin, a.batch):
        bs = starts[i:i+a.batch]
        x = torch.stack([ho[s:s+a.block] for s in bs]).to(dev)
        y = torch.stack([ho[s+1:s+1+a.block] for s in bs]).to(dev)
        with torch.autocast("cuda", dtype=torch.bfloat16):
            _, l = model(x, y)
        sum_loss += l.item() * len(bs); nseen += len(bs)
mean_loss = sum_loss / max(1, nseen)
ppl = math.exp(mean_loss)
out = {"train_npy": a.train_npy, "heldout_ppl": ppl, "mean_loss": mean_loss,
       "max_iters": a.max_iters, "seed": a.seed, "train_s": round(train_s, 1)}
json.dump(out, open(a.out_json, "w"))
print(f"HELDOUT_PPL {ppl:.3f}  loss {mean_loss:.4f}  train_s {train_s:.0f}  {a.train_npy}")
-r--r--r-- 1 root root 2.0M Aug  3 22:34 data/multi_dev.npy
-r--r--r-- 1 root root 773M Aug  3 22:35 data/pool.jsonl

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Wall time 4.4 seconds
Output:
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Wall time 7.7 seconds
Output:

---  Beardmore was sufficiently impressed with Shackleton to offer financial support , but other donations proved hard to come by . Nevertheless , in February 1907 , Shackleton presented to the Royal Geographic Society his plans for an Antarctic expedition , the details of which , under the name British Antarctic Expedition , were published in the Royal Society 's newsletter , Geographic Journal . The aim was the conquest of both the geographical South Pole and the South Magnetic Pole . Shackleton then worked hard to persuade others of his wealthy friends and acquaintances to contribute , including Sir Philip Lee Brocklehurst , who subscribed £ 2 @,@ 000 ( 2011 equivalent £ 157 @,@ 000 ) to secure a place on the expedition ; author Campbell Mackellar ; and Guinness baron Lord Iveagh , whose contribution was secured less than two weeks before the departure of the expedition ship Nimrod . On 4 August 1907 , Shackleton was appointed a Member of the Royal Victorian Order , 4th Class ( MVO ; the present @-@ day grade of Lieutenant ) .  <|endoftext|> On 1 January 1908 , Nimrod sailed for the Antarctic from Lyttelton Harbour , New Zealand . Shackleton 's original plans had envisaged using the

---  " remaining , now open to him .  <|endoftext|> Shackleton published details of his new expedition , grandly titled the " Imperial Trans @-@ Antarctic Expedition " , early in 1914 . Two ships would be employed ; Endurance would carry the main party into the Weddell Sea , aiming for Vahsel Bay from where a team of six , led by Shackleton , would begin the crossing of the continent . Meanwhile , a second ship , the Aurora , would take a supporting party under Captain Aeneas Mackintosh to McMurdo Sound on the opposite side of the continent . This party would then lay supply depots across the Great Ice Barrier as far as the Beardmore Glacier , these depots holding the food and fuel that would enable Shackleton 's party to complete their journey of 1 @,@ 800 miles ( 2 @,@ 900 km ) across the continent .  <|endoftext|> Shackleton used his considerable fund @-@ raising skills , and the expedition was financed largely by private donations , although the British government gave £ 10 @,@ 000 ( about £ 680 @,@ 000 in 2008 terms ) . Scottish jute magnate Sir James Caird gave £ 24 @,@ 000 , Midlands industrialist Frank Dudley Docker gave £ 10 @,@ 000 and tobacco heiress Janet Stancomb @-@ Wills

---  50 feet of rope between them — and a carpenter 's adze " .  <|endoftext|> Shackleton immediately sent a boat to pick up the three men from the other side of South Georgia while he set to work to organise the rescue of the Elephant Island men . His first three attempts were foiled by sea ice , which blocked the approaches to the island . He appealed to the Chilean government , which offered the use of Yelcho , a small seagoing tug from its navy . Yelcho , commanded by Captain Luis Pardo , and the British whaler SS Southern Sky reached Elephant Island on 30 August 1916 , at which point the men had been isolated there for four and a half months , and Shackleton quickly evacuated all 22 men . The Yelcho took the crew first to Punta Arenas and after some days to Valparaiso in Chile where crowds warmly welcomed them back to civilisation .  <|endoftext|> There remained the men of the Ross Sea Party , who were stranded at Cape Evans in McMurdo Sound , after Aurora had been blown from its anchorage and driven out to sea , unable to return . The ship , after a drift of many months , had returned to New Zealand . Shackleton travelled there to join Aurora , and sailed with her to the rescue o

---  surged while that of his erstwhile rival declined . In 2002 , in a BBC poll conducted to determine the " 100 Greatest Britons " , Shackleton was ranked 11th while Scott was down in 54th place . In 2007 , the Shackleton Foundation was founded to honour the legacy of Ernest Shackleton by supporting inspirational leaders who exemplify his indomitable spirit and strive to make a positive difference to the world .  <|endoftext|> In 2001 Margaret Morrell and Stephanie Capparell presented Shackleton as a model for corporate leadership in their book Shackleton 's Way : Leadership Lessons from the Great Antarctic Explorer . They wrote : " Shackleton resonates with executives in today 's business world . His people @-@ centred approach to leadership can be a guide to anyone in a position of authority " . Other management writers were soon following this lead , using Shackleton as an exemplar for bringing order from chaos . The Centre for Leadership Studies at the University of Exeter ( United Kingdom ) offers a course on Shackleton , who also features in the management education programmes of several American universities . In Boston USA a " Shackleton School " was set up on " Outward Bound

---  showing Brian falling asleep when watching Late Night with Bib Fortuna while Stewie was still in the tanning booth , but it did not seem good enough to air , so it was never broadcast . When Stewie is being sprayed by Brian with sun @-@ screen , Ralph Furley enters the room and mistakenly thinks he sees Brian ejaculating on Stewie . The cream was originally intended to keep squirting out of the bottle as if Brian were continuing to ejaculate despite Mr. Furley 's presence , but the animation didn 't look right and was removed . MacFarlane comments that he was amazed that broadcasting standards allowed them to get away with the scene , even though Brian 's line , " This isn 't what it looks like " was edited to keep the ejaculation illusion from being blatant ( the TV version goes from Mr. Furley yelling , " Brian " and Brian reacting to Mr. Furley running out and saying , " Never mind , I 'll come back later ! " ) . A deleted scene was made and designed to happen directly after Peter sickens Meg by farting in her face in which Peter steals Chris 's lunch money and runs for the bus . The gag showing Dick Cheney as a Wal @-@ Mart greeter had Cheney 's greeting , " Go fuck yourself "

---  considerably more than Steele 's deliberate underestimate . The ship was launched on 8 August 1945 after being named by Steele 's wife , and later became the largest ship to be commissioned by the Australian Army during World War II . Construction of a sister ship , to be called AV2768 Corsair , was also begun , but this ship was cancelled when the war ended .  <|endoftext|> The ship completed her sea trials in late November 1945 , and subsequently entered service with the Army 's No. 2 Ordnance Craft Park . In February 1946 Crusader sailed to Rabaul in New Britain and later Torokina , Bougainville . During these and later voyages she proved successful in her intended role , and returned supplies and equipment from the islands to Australia . She also transported the bodies of 600 Australian servicemen killed during the fighting in the Solomon Islands to Port Moresby for permanent interment in the war cemetery there . Other unusual tasks undertaken by the vessel included transporting 800 native New Guineans from Aitape , Madang , Torokina and Wewak to a dispersal centre located in Rabaul and moving 44 tanks from Torokina to Sydney .  <|endoftext|> By January 1947 the Army no longer
NVIDIA H100 80GB HBM3, 81076 MiB

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--- 0 " Beardmore was sufficiently impressed with Shackleton to offer financial support , but other donations proved hard to come by . Nevertheless , in February 1907 , Shackleton presented to the Royal Geographic Society his plans for an Antarctic expedition , the details of which , under the name British Antarctic Expedition , were published in the Royal Society 's newsletter , Geographic Journal . The aim was the conquest of both the geographical South Pole and the South Magnetic Pole . Shackleton then worked hard to persuade others of his wealthy friends and acquaintances to contribute , including Sir Philip Lee Brocklehurst , who subscribed £ 2 @,@ 000 ( 2011 equivalent £ 157 @,@ 000 ) to secure a place on the expedition ; author Campbell Mackellar ; and Guinness baron Lord Iveagh , whose contribution was secured less than two weeks before the departure of the expedition ship Nimrod . On 4 August 1907 , Shackleton was appointed a Member of the Royal Victorian Order , 4th Class ( MVO ; the present @-@ day grade of Lieutenant ) .  <|endoftext|> On 1 January 1908 , Nimrod sailed for the Antarctic from Lyttelton Harbour , New Zealand . Shackleton 's original plans had envisaged using the old Discovery base in McMurdo Sound to launch his attempts on the South Pole and South Magnetic Pole . However , before leaving England , he had been pressured to give an undertaking to Scott that he would not base himself in the McMurdo area , which Scott was claiming as his own field of work . Sh"

--- 250000 "Description of a very high speed transit (VHST) system operating in its own rarefied atmosphere in evacuated tubes in underground tunnels. Most cases considered took less time to go coast-to-coast (e.g., 21 min) than it takes an aircraft to climb to an efficient operating altitude. VHST's tubecraft ride on, and are driven by, electromagnetic (EM) waves. In accelerating, it employs the energy of the surrounding EM field; in decelerating, it returns most of this energy to the system. Tunnel systems would be shared by oil, water, and gas pipelines; channels for laser and microwave waveguides; electric power lines including superconducting ones; and freight systems. Environmental and economic benefits are substantial, and the technology for building and operating the system exists.  This report is part of the RAND Corporation paper series. The paper was a product of the RAND Corporation from 1948 to 2003 that captured speeches, memorials, and derivative research, usually prepared on authors' own time and meant to be the scholarly or scientific contribution of individual authors to their professional fields. Papers were less formal than reports and did not require rigorous peer review.  Permission is given to duplicate this electronic document for personal use only, as long as it is unaltered and complete. Copies may not be duplicated for commercial purposes. Unauthorized posting of RAND PDFs to a non-RAND Web site is prohibited. RAND PDFs are protected under copyright law. For in"

--- 500000 'I fucking hate when he does this shit pic.twitter.com/kpmcHnW4Cz — Miley Ray Cyrus (@MileyCyrus) April 22, 2018 Singer-actress Miley Cyrus has shared a rare video of herself with her partner, actor Liam Hemsworth, on social media. She shared a video of them both in a car, which showed Hemsworth listening to rap music as he drove them to their destination.Cyrus captioned the image: "I f***ing hate it when he does this s**t."In the clip, she was sporting a top which had the word \'Sunday\' emblazoned across the front as she sat in the passenger seat. With his music blaring out, she filmed herself dancing and bopping her head to the music. Then all of a sudden, he made Cyrus jump as he suddenly screamed at her, causing to drop her phone in panic.(With IANS inputs)<|endoftext|>Uttar Pradesh chief minister Yogi Adityanath has maintained that 63 children in government-run Gorakhpur hospital died because of their ailments and not oxygen shortage, but grieving parents say their children were fine till the oxygen supply was cut.Some like Mohd Zahid, father of a five-year-old, alleged that BRD hospital authorities refused to declare their children dead, even as their bodies had turned ice cold as this would have further taken up the death toll.Another father said that while his son started bleeding from the nose, the hospital staff dismissed it saying, “Kachra nikal raha hai” (It’s just body waste that is coming out).In a harrowing tragedy exposing the sorry state of medical facilities i'

--- 750000 '<p>I found the platform module but it says it returns \'Windows\' and it\'s returning \'Microsoft\' on my machine. I notice in another thread here on stackoverflow it returns \'Vista\' sometimes.</p> <p>So, the question is, how do implemement?</p> <pre><code>if is_windows():   ... </code></pre> <p>In a forward compatible way? If I have to check for things like \'Vista\' then it will break when the next version of windows comes out.</p> <hr /> <p>Note: The answers claiming this is a duplicate question do not actually answer the question <code>is_windows</code>. They answer the question &quot;what platform&quot;. Since many flavors of windows exist none of them comprehensively describe how to get an answer of <code>isWindows</code>.</p>  <p>Python <a href="http://docs.python.org/library/os.html" rel="noreferrer">os</a> module</p>  <p>Specifically for Python 3.6/3.7:</p>  <blockquote>   <p><code>os.name</code>: The name of the operating   system dependent module imported. The   following names have currently been   registered: \'posix\', \'nt\', \'java\'.</p> </blockquote>  <p>In your case, you want to check for \'nt\' as <code>os.name</code> output:</p>  <pre><code>import os  if os.name == \'nt\':      ... </code></pre>  <p>There is also a note on <code>os.name</code>:</p>  <blockquote>   <p>See also <a href="https://docs.python.org/3.5/library/sys.html#sys.platform" rel="noreferrer"><code>sys.platform</code></a> has a finer granularity. <a href="https://docs.python.org/3.5/library/os.html#os.una'

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{"id": 28721, "text": "++ I'm commenting mostly just to bump this excellent piece of advice.\nSince port is rarely important and I like to use this idiom in addition to running a traditional webserver on port 80, I'd shorten it to use the default port 5000\u2013\nplackup -L Shotgun -MPlack::App::WrapCGI -e \"Plack::App::WrapCGI->new(script => shift)\" [cgi]\n\u2013which lends itself to an alias\u2
{"id": 88358, "text": "<|endoftext|>OO in the real world....\n- Wrench set\n- Socket Set\n- Screwdriver set\n- Pots and pans\nOO exists in many place in many ways. It is great in situations where there are large unknowns in the future. You build code, test it and maintain it seperately. You assign rules (parameters) to its input and output. Others that use it follow these rules do not worry as muc
{"id": 91439, "text": "Sorry that this reply is almost boiler-plate, but...\n- Does that site's usage guidance permit scraping?\n- Do you have authority/permission to extract data?\n- Does the site publish an API you could use rather than rolling your own?\n- Assuming that by \"the number 1975\" you mean you're looking for white males, DOB in 1923 and resident of San Diego Cty in 1940, who still t

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./pack_selection.py:1:"""Solver-side: pack a selection.json (ordered ids) into a 12M-token train array from pool.jsonl."""
./pack_selection.py:6:SEL = sys.argv[1] if len(sys.argv) > 1 else "/workspace/submission/selection.json"
./pack_selection.py:10:tok = AutoTokenizer.from_pretrained("gpt2"); EOS = tok.eos_token_id
./pack_selection.py:17:    ids = tok(text[i], add_special_tokens=False).input_ids
./pack_selection.py:22:print(f"packed {len(arr)} tokens from {len(sel)} ids -> {OUT}")
./model.py:5:tokenizer. Standard pre-norm Transformer with weight-tied embeddings.
./model.py:113:        next-token cross-entropy if `targets` given, else None. The distillation
./model.py:144:        """Mean per-token NLL over `n_batches` eval batches -> perplexity."""
./train_nano.py:1:"""From-scratch nano-GPT (30M) LM training on a 1D token .npy; report held-out perplexity.
./train_nano.py:2:Frozen recipe — the ONLY thing that varies between runs is the training token set."""
./train_nano.py:50:# held-out perplexity: mean next-token CE over non-overlapping windows (window-weighted)

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  File "/workspace/submission/curate.py", line 14, in <module>
    from sklearn.feature_extraction.text import HashingVectorizer
ModuleNotFoundError: No module named 'sklearn'

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  File "/workspace/submission/curate.py", line 14, in <module>
    from sklearn.feature_extraction.text import HashingVectorizer
ModuleNotFoundError: No module named 'sklearn'

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pip 26.2 from /usr/local/lib/python3.10/dist-packages/pip (python 3.10)

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Warning: truncated output (original token count: 6044)
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ls: cannot access 'submission/selection.json': No such file or directory
5189
190 bash -c  rm -f /tmp/ri-exec-36f1d5c574654fff9e2406a5f6543ae0.pid setsid bash -c 'set -o pipefail;  export PATH="$HOME/.local/bin:/usr/local/bin:$PATH" if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi OUT=/logs/agent/codex.txt mkdir -p "$(dirname "$OUT")" : > "$OUT" ATT="$(mktemp /tmp/codex-attempt.XXXXXX)" trap '"'"'rm -f "$ATT"'"'"' EXIT  read_remaining() {   local r=""   if [ -r /workspace/.timer/remaining_secs ]; then     r="$(cat /workspace/.timer/remaining_secs 2>/dev/null)"   fi   if ! [ "$r" -ge 0 ] 2>/dev/null; then r="${TASK_BUDGET_SECS:-}"; fi   if ! [ "$r" -ge 0 ] 2>/dev/null; then r=86400; fi   echo "$r" }  MIN_REM=90 MAX_RESUMES=15 BASE=20 CAP=240 JIT=25 N=0 rc=1 while : ; do   REM="$(read_remaining)"   if [ "$REM" -le "$MIN_REM" ] 2>/dev/null; then     echo "=== codex: budget exhausted (remaining=${REM}s); stopping ===" | tee -a "$OUT"     break   fi   : > "$ATT"   if [ "$N" -eq 0 ]; then     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=low -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 …4044 tokens truncated…"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. 
696 python submission/curate.py
767 /bin/bash -c ls -lh submission/selection.json; cat .timer/remaining_secs; pgrep -af curate || true

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