claude-opus-5

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[stdout]
ls: cannot access '/tmp/nojunk.json': No such file or directory
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[metadata] {"noOutputExpected": false}

154 bash -c  rm -f /tmp/ri-exec-1e6a5466a5e54f48a655f70cf0d6a6a4.pid setsid bash -c 'set -o pipefail; export PATH="$HOME/.local/bin:/usr/local/bin:$PATH"; claude --verbose --output-format=stream-json --permission-mode=bypassPermissions --disallowedTools WebSearch WebFetch --print -- '"'"'# 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. '"'"' 2>&1 </dev/null | tee /logs/agent/claude-code.txt' & child="$!" echo "$child" > /tmp/ri-exec-1e6a5466a5e54f48a655f70cf0d6a6a4.pid wait "$child" rc="$?" rm -f /tmp/ri-exec-1e6a5466a5e54f48a655f70cf0d6a6a4.pid exit "$rc" 
156 bash -c set -o pipefail; export PATH="$HOME/.local/bin:/usr/local/bin:$PATH"; claude --verbose --output-format=stream-json --permission-mode=bypassPermissions --disallowedTools WebSearch WebFetch --print -- '# 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. ' 2>&1 </dev/null | tee /logs/agent/claude-code.txt
157 claude --verbose --output-format=stream-json --permission-mode=bypassPermissions --disallowedTools WebSearch WebFetch --print -- # 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. 
3233 bash -c for v in v1 sel_pure sel_wq sel_nogate rand; do for s in 555 999; do python train_nano.py --train_npy /tmp/${v}_train.npy --heldout_npy data/multi_dev.npy --out_json /tmp/x.json --seed $s 2>&1|grep HELDOUT|sed "s|^|$v s=$s |"; done; done
3293 python train_nano.py --train_npy /tmp/v1_train.npy --heldout_npy data/multi_dev.npy --out_json /tmp/x.json --seed 999
3327 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1785799565856-bsp58f.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'pgrep -af train_nano | head; pgrep -af "for v in" | head -3' < /dev/null && pwd -P >| /tmp/claude-5723-cwd
3233 bash -c for v in v1 sel_pure sel_wq sel_nogate rand; do for s in 555 999; do python train_nano.py --train_npy /tmp/${v}_train.npy --heldout_npy data/multi_dev.npy --out_json /tmp/x.json --seed $s 2>&1|grep HELDOUT|sed "s|^|$v s=$s |"; done; done
3327 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1785799565856-bsp58f.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'pgrep -af train_nano | head; pgrep -af "for v in" | head -3' < /dev/null && pwd -P >| /tmp/claude-5723-cwd

[stdout]
154 bash -c  rm -f /tmp/ri-exec-1e6a5466a5e54f48a655f70cf0d6a6a4.pid setsid bash -c 'set -o pipefail; export PATH="$HOME/.local/bin:/usr/local/bin:$PATH"; claude --verbose --output-format=stream-json --permission-mode=bypassPermissions --disallowedTools WebSearch WebFetch --print -- '"'"'# 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. '"'"' 2>&1 </dev/null | tee /logs/agent/claude-code.txt' & child="$!" echo "$child" > /tmp/ri-exec-1e6a5466a5e54f48a655f70cf0d6a6a4.pid wait "$child" rc="$?" rm -f /tmp/ri-exec-1e6a5466a5e54f48a655f70cf0d6a6a4.pid exit "$rc" 
156 bash -c set -o pipefail; export PATH="$HOME/.local/bin:/usr/local/bin:$PATH"; claude --verbose --output-format=stream-json --permission-mode=bypassPermissions --disallowedTools WebSearch WebFetch --print -- '# 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. ' 2>&1 </dev/null | tee /logs/agent/claude-code.txt
157 claude --verbose --output-format=stream-json --permission-mode=bypassPermissions --disallowedTools WebSearch WebFetch --print -- # 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. 
3233 bash -c for v in v1 sel_pure sel_wq sel_nogate rand; do for s in 555 999; do python train_nano.py --train_npy /tmp/${v}_train.npy --heldout_npy data/multi_dev.npy --out_json /tmp/x.json --seed $s 2>&1|grep HELDOUT|sed "s|^|$v s=$s |"; done; done
3293 python train_nano.py --train_npy /tmp/v1_train.npy --heldout_npy data/multi_dev.npy --out_json /tmp/x.json --seed 999
3327 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1785799565856-bsp58f.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'pgrep -af train_nano | head; pgrep -af "for v in" | head -3' < /dev/null && pwd -P >| /tmp/claude-5723-cwd
3233 bash -c for v in v1 sel_pure sel_wq sel_nogate rand; do for s in 555 999; do python train_nano.py --train_npy /tmp/${v}_train.npy --heldout_npy data/multi_dev.npy --out_json /tmp/x.json --seed $s 2>&1|grep HELDOUT|sed "s|^|$v s=$s |"; done; done
3327 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1785799565856-bsp58f.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'pgrep -af train_nano | head; pgrep -af "for v in" | head -3' < /dev/null && pwd -P >| /tmp/claude-5723-cwd
[metadata] {"noOutputExpected": false}

register token mix: [0.25 0.25 0.25 0.25]
wrote /tmp/nojunk.json
register token mix: [0.299 0.299 0.299 0.104]
wrote /tmp/lightgate.json
register token mix: [1. 0. 0. 0.]
wrote /tmp/nojunkrand.json

[stdout]
register token mix: [0.25 0.25 0.25 0.25]
wrote /tmp/nojunk.json
register token mix: [0.299 0.299 0.299 0.104]
wrote /tmp/lightgate.json
register token mix: [1. 0. 0. 0.]
wrote /tmp/nojunkrand.json
[metadata] {"noOutputExpected": false}

3678

[stdout]
3678
[metadata] {"noOutputExpected": false}

File created successfully at: /workspace/claim.md (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/workspace/claim.md", "content": "# Claim: register-balanced coverage beats quality filtering at a 12M-token budget\n\n*(numbers are dev perplexity on `data/multi_dev.npy` with the frozen trainer, seed 1337\nunless stated; the random-selection reference measured here is **469.9**)*\n\n## Hypothesis\n\nFor a 30M-parameter LM trained from scratch on only 12M tokens, held-out perplexity on a\nbroad four-register target (encyclopedic / high-quality web prose / news / technical Q&A)\nis governed by **how completely and how evenly the training set covers those four\nregisters**, and *not* by how aggressively low-quality documents are filtered out.\n\nConcretely I claim two things:\n\n1. **Register balance is the effective lever.** Assigning every pool document to the\n   target register it most resembles (log-odds against a raw-pool background class, from\n   a bag-of-hashed-character-4-grams classifier trained on the *decoded, format-stripped*\n   disclosed target) and then interleaving the four ranked lists **by token count**, so\n   that the 12M-token prefix holds ~25% of each register, lowers perplexity substantially\n   below a random selection.\n2. **Extra \"quality\" gating is actively harmful past a very low bar.** Beyond a minimum\n   document length (~1000 characters), each additional narrowing of the candidate pool\n   *raises* perplexity, because at this budget the model is still learning generic\n   English and a narrower pool means fewer distinct topics and phrasings per token spent.\n\n## Mechanism\n\nThe model sees 24.6M training tokens (3000 iters x 32 x 256) drawn from a 12M-token set —\nabout two epochs. In that regime almost all of the achievable loss reduction comes from\ngeneric English statistics *in the target's registers*; capacity spent on a register the\ntarget does not contain is wasted, but so is coverage lost to over-filtering. A quality\nfilter with a target-shaped acceptance region (my Mahalanobis \"surface-distribution\" gate)\nremoves documents that are perfectly good English simply because their character\nstatistics sit in the tail, and what it removes is disproportionately the *diversity* the\nmodel needs.\n\n### Observables predicted, other than the final perplexity\n\n1. **The pool is mostly \"general high-quality web prose.\"** A 5-way classifier\n   (4 target registers + raw-pool background) should separate the encyclopedic, news and\n   Q&A registers from the pool but should *fail* on the general-web-prose register,\n   because that register and the pool are the same distribution.\n   *Observed held-out accuracy: wiki 0.97, qa 0.94, news 0.79–0.84, pool 0.97,\n   general-web-prose 0.11–0.35 (i.e. at/below chance vs. the pool class).*\n2. **Top-ranked documents are recognisably the right register on inspection.**\n   *Observed (`peek.py`): the wiki list returns Britannica/Wikipedia-style entries\n   (\"Anglo-Dutch Wars, also called Dutch Wars…\"), the news list returns wire copy\n   (\"LUCKNOW, India (Reuters) —…\"), the Q&A list returns programming help threads\n   (servlet `getParameter()` quoting, `.sql` deployment scripts).*\n3. **Monotone filter-strictness curve.** Perplexity should increase monotonically as the\n   surface-distribution gate is tightened, all else equal.\n   *Observed, at min-length 1000 chars with identical register balancing:\n   keep-fraction 1.00 → **404.6**, 0.70 → 449.5, 0.40 → 497.3.*\n4. **Non-monotone document-length curve with an interior optimum.** Forcing very short\n   documents starves each 256-token window of coherent context; forcing very long ones\n   buys fewer distinct topics per token.\n   *Observed mean selected document length vs. PPL: 496 tok → 540.2, 780 → 493.8,\n   947 → 449.5, 1874 → 469.2, 3622 → 478.5.*\n5. **Balance-vs-order ablation.** Replacing the register-balanced order with a random\n   order over the *same* candidate pool should lose most of the gain.\n   *Observed: see the ablation table below.*\n\n## Falsification\n\nThe claim is falsified if any of these hold:\n\n* A random order over the same minimum-length candidate pool matches the register-balanced\n  order (would mean the classifier and the mixture contribute nothing, and the entire\n  effect is the length floor).\n* Tightening the surface-distribution gate *lowers* perplexity, or a strict\n  quality-filtered selection beats the unfiltered-but-balanced one.\n* Perplexity is monotone in mean document length (would refute the interior optimum, and\n  the \"diversity per token\" mechanism behind it).\n* The 5-way classifier separates general-web-prose from the pool as well as it separates\n  Wikipedia (would refute observable 1, i.e. the pool is *not* mostly that register, and\n  the balance argument would need re-deriving).\n\nNote on measurement precision: retraining the *same* 12M-token set with a different seed\nmoved dev PPL by ~12 (449.5 → 437.8), so only differences of roughly >25 PPL are treated\nas real above; the strictness and length effects cited are 45–90 PPL.\n\n## Transfer\n\nThe recipe needs no labels and no reference LM, so it transfers to any budgeted\npretraining-data curation where the evaluation target is disclosed as a mixture:\n\n1. Decode the target, **strip corpus-specific surface artifacts** (here WikiText's\n   ` @,@ ` escapes and spaced punctuation, StackExchange's HTML tags and entities) —\n   otherwise the classifier keys on formatting that does not exist in the pool and the\n   register signal is lost;\n2. train a cheap bag-of-hashed-4-grams classifier of *target register vs. raw pool*\n   (seconds on one GPU, no tokenizer needed);\n3. rank inside each register, then interleave **by token count** so that every prefix of\n   the priority list — including whatever prefix the budget happens to cut at — carries\n   the target's mixture;\n4. filter only for junk you can defend (length floor, non-English, duplicated lines), and\n   verify with an ablation that each filter you add is not costing you coverage.\n\nThe same \"balance by token share, filter minimally\" conclusion should hold for any\ncompute-starved run in this regime, and the classifier is reusable as a router for\nmixing weights when the target mixture is known but unbalanced.\n", "structuredPatch": [], "originalFile": null, "userModified": false}

3638

[stdout]
3638
[metadata] {"noOutputExpected": false}

GATEONLY-RANDORDER HELDOUT_PPL 488.677  loss 6.1917  train_s 56  /tmp/gateonly_train.npy
/tmp/nojunk.json: packed 12000000 tok, 10029 docs, mean doc 1197 tok -> /tmp/nojunk_train.npy
/tmp/lightgate.json: packed 12000000 tok, 14377 docs, mean doc 835 tok -> /tmp/lightgate_train.npy
/tmp/nojunkrand.json: packed 12000000 tok, 8767 docs, mean doc 1369 tok -> /tmp/nojunkrand_train.npy
nojunk HELDOUT_PPL 341.255  loss 5.8326  train_s 56  /tmp/nojunk_train.npy

[stdout]
GATEONLY-RANDORDER HELDOUT_PPL 488.677  loss 6.1917  train_s 56  /tmp/gateonly_train.npy
/tmp/nojunk.json: packed 12000000 tok, 10029 docs, mean doc 1197 tok -> /tmp/nojunk_train.npy
/tmp/lightgate.json: packed 12000000 tok, 14377 docs, mean doc 835 tok -> /tmp/lightgate_train.npy
/tmp/nojunkrand.json: packed 12000000 tok, 8767 docs, mean doc 1369 tok -> /tmp/nojunkrand_train.npy
nojunk HELDOUT_PPL 341.255  loss 5.8326  train_s 56  /tmp/nojunk_train.npy
[metadata] {"noOutputExpected": false}

wrote /tmp/nj2000.json
wrote /tmp/nj400.json
register token mix: [0.2 0.2 0.2 0.4]
wrote /tmp/njqa.json
nojunk HELDOUT_PPL 341.255  loss 5.8326  train_s 56  /tmp/nojunk_train.npy
lightgate HELDOUT_PPL 416.387  loss 6.0316  train_s 56  /tmp/lightgate_train.npy

[stdout]
wrote /tmp/nj2000.json
wrote /tmp/nj400.json
register token mix: [0.2 0.2 0.2 0.4]
wrote /tmp/njqa.json
nojunk HELDOUT_PPL 341.255  loss 5.8326  train_s 56  /tmp/nojunk_train.npy
lightgate HELDOUT_PPL 416.387  loss 6.0316  train_s 56  /tmp/lightgate_train.npy
[metadata] {"noOutputExpected": false}

lightgate HELDOUT_PPL 416.387  loss 6.0316  train_s 56  /tmp/lightgate_train.npy
3458

[stdout]
lightgate HELDOUT_PPL 416.387  loss 6.0316  train_s 56  /tmp/lightgate_train.npy
3458
[metadata] {"noOutputExpected": false}

The file /workspace/submission/curate.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/submission/curate.py", "oldString": "ap.add_argument(\"--out\", default=\"/workspace/submission/selection.json\")\nap.add_argument(\"--min_chars\", type=int, default=4000)\nap.add_argument(\"--max_chars\", type=int, default=200_000)\nap.add_argument(\"--keep_frac\", type=float, default=0.70)", "newString": "ap.add_argument(\"--out\", default=\"/workspace/submission/selection.json\")\nap.add_argument(\"--min_chars\", type=int, default=1000)\nap.add_argument(\"--max_chars\", type=int, default=200_000)\n# --- ablation switches; both OFF in the submitted configuration (see claim.md):\n#     every extra filter measurably RAISES dev perplexity at this token budget.\nap.add_argument(\"--junk_gate\", action=\"store_true\",\n                help=\"ablation: also apply the boilerplate/fluency heuristic gate\")\nap.add_argument(\"--keep_frac\", type=float, default=1.0,\n                help=\"ablation: keep only this quantile of documents closest to the \"\n                     \"target's surface statistics (1.0 = gate disabled)\")", "originalFile": null, "structuredPatch": [{"oldStart": 64, "oldLines": 9, "newStart": 64, "newLines": 15, "lines": [" ", " ap = argparse.ArgumentParser()", " ap.add_argument(\"--out\", default=\"/workspace/submission/selection.json\")", "-ap.add_argument(\"--min_chars\", type=int, default=4000)", "+ap.add_argument(\"--min_chars\", type=int, default=1000)", " ap.add_argument(\"--max_chars\", type=int, default=200_000)", "-ap.add_argument(\"--keep_frac\", type=float, default=0.70)", "+# --- ablation switches; both OFF in the submitted configuration (see claim.md):", "+#     every extra filter measurably RAISES dev perplexity at this token budget.", "+ap.add_argument(\"--junk_gate\", action=\"store_true\",", "+                help=\"ablation: also apply the boilerplate/fluency heuristic gate\")", "+ap.add_argument(\"--keep_frac\", type=float, default=1.0,", "+                help=\"ablation: keep only this quantile of documents closest to the \"", "+                     \"target's surface statistics (1.0 = gate disabled)\")", " ap.add_argument(\"--mix\", default=\"0.25,0.25,0.25,0.25\")", " ap.add_argument(\"--target_tokens\", type=int, default=26_000_000)   # ~2x the 12M budget", " ap.add_argument(\"--nstep\", type=int, default=1200)"]}], "userModified": false, "replaceAll": false}

The file /workspace/submission/curate.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/workspace/submission/curate.py", "oldString": "    f = lambda n: feats[:, IDX[n]]\n    keep = ((f(\"nchar\") >= A.min_chars) & (f(\"nchar\") <= A.max_chars) &\n            (f(\"stop_frac\") >= 0.18) & (f(\"stop_frac\") <= 0.62) &\n            (f(\"alpha_frac\") >= 0.62) & (f(\"digit_frac\") <= 0.08) &\n            (f(\"upper_frac\") <= 0.12) & (f(\"nonascii_frac\") <= 0.03) &\n            (f(\"sym_frac\") <= 0.05) & (f(\"mean_wordlen\") >= 3.3) & (f(\"mean_wordlen\") <= 6.5) &\n            (f(\"uniq_line_frac\") >= 0.75) & (f(\"endpunct_frac\") >= 0.45) &\n            (f(\"bullet_frac\") <= 0.20) & (f(\"ellipsis_frac\") <= 0.08) &\n            (f(\"chars_per_line\") >= 90) & (f(\"space_frac\") >= 0.12) & (f(\"space_frac\") <= 0.26))\n    print(f\"junk gate keeps {keep.sum()} / {N}\")\n\n    X = feats[:, [IDX[n] for n in DIST_FEATS]]\n    dist = np.sqrt((((X - mu) / sd) ** 2).mean(1))\n    keep &= dist <= np.quantile(dist[keep], A.keep_frac)\n    print(f\"+ surface-distribution gate keeps {keep.sum()}\")", "newString": "    f = lambda n: feats[:, IDX[n]]\n    keep = (f(\"nchar\") >= A.min_chars) & (f(\"nchar\") <= A.max_chars)   # length floor only\n    print(f\"length floor keeps {keep.sum()} / {N}\")\n\n    if A.junk_gate:          # ablation only -- costs ~64 dev PPL, see claim.md\n        keep &= ((f(\"stop_frac\") >= 0.18) & (f(\"stop_frac\") <= 0.62) &\n                 (f(\"alpha_frac\") >= 0.62) & (f(\"digit_frac\") <= 0.08) &\n                 (f(\"upper_frac\") <= 0.12) & (f(\"nonascii_frac\") <= 0.03) &\n                 (f(\"sym_frac\") <= 0.05) & (f(\"mean_wordlen\") >= 3.3) &\n                 (f(\"mean_wordlen\") <= 6.5) & (f(\"uniq_line_frac\") >= 0.75) &\n                 (f(\"endpunct_frac\") >= 0.45) & (f(\"bullet_frac\") <= 0.20) &\n                 (f(\"ellipsis_frac\") <= 0.08) & (f(\"chars_per_line\") >= 90) &\n                 (f(\"space_frac\") >= 0.12) & (f(\"space_frac\") <= 0.26))\n        print(f\"+ junk gate keeps {keep.sum()}\")\n\n    if A.keep_frac < 1.0:    # ablation only -- costs ~45-90 dev PPL, see claim.md\n        X = feats[:, [IDX[n] for n in DIST_FEATS]]\n        dist = np.sqrt((((X - mu) / sd) ** 2).mean(1))\n        keep &= dist <= np.quantile(dist[keep], A.keep_frac)\n        print(f\"+ surface-distribution gate keeps {keep.sum()}\")", "originalFile": null, "structuredPatch": [{"oldStart": 261, "oldLines": 21, "newStart": 261, "newLines": 26, "lines": ["     S, sig = register_scores(prefix, tg)", " ", "     f = lambda n: feats[:, IDX[n]]", "-    keep = ((f(\"nchar\") >= A.min_chars) & (f(\"nchar\") <= A.max_chars) &", "-            (f(\"stop_frac\") >= 0.18) & (f(\"stop_frac\") <= 0.62) &", "-            (f(\"alpha_frac\") >= 0.62) & (f(\"digit_frac\") <= 0.08) &", "-            (f(\"upper_frac\") <= 0.12) & (f(\"nonascii_frac\") <= 0.03) &", "-            (f(\"sym_frac\") <= 0.05) & (f(\"mean_wordlen\") >= 3.3) & (f(\"mean_wordlen\") <= 6.5) &", "-            (f(\"uniq_line_frac\") >= 0.75) & (f(\"endpunct_frac\") >= 0.45) &", "-            (f(\"bullet_frac\") <= 0.20) & (f(\"ellipsis_frac\") <= 0.08) &", "-            (f(\"chars_per_line\") >= 90) & (f(\"space_frac\") >= 0.12) & (f(\"space_frac\") <= 0.26))", "-    print(f\"junk gate keeps {keep.sum()} / {N}\")", "+    keep = (f(\"nchar\") >= A.min_chars) & (f(\"nchar\") <= A.max_chars)   # length floor only", "+    print(f\"length floor keeps {keep.sum()} / {N}\")", " ", "-    X = feats[:, [IDX[n] for n in DIST_FEATS]]", "-    dist = np.sqrt((((X - mu) / sd) ** 2).mean(1))", "-    keep &= dist <= np.quantile(dist[keep], A.keep_frac)", "-    print(f\"+ surface-distribution gate keeps {keep.sum()}\")", "+    if A.junk_gate:          # ablation only -- costs ~64 dev PPL, see claim.md", "+        keep &= ((f(\"stop_frac\") >= 0.18) & (f(\"stop_frac\") <= 0.62) &", "+                 (f(\"alpha_frac\") >= 0.62) & (f(\"digit_frac\") <= 0.08) &", "+                 (f(\"upper_frac\") <= 0.12) & (f(\"nonascii_frac\") <= 0.03) &", "+                 (f(\"sym_frac\") <= 0.05) & (f(\"mean_wordlen\") >= 3.3) &", "+                 (f(\"mean_wordlen\") <= 6.5) & (f(\"uniq_line_frac\") >= 0.75) &", "+                 (f(\"endpunct_frac\") >= 0.45) & (f(\"bullet_frac\") <= 0.20) &", "+                 (f(\"ellipsis_frac\") <= 0.08) & (f(\"chars_per_line\") >= 90) &", "+                 (f(\"space_frac\") >= 0.12) & (f(\"space_frac\") <= 0.26))", "+        print(f\"+ junk gate keeps {keep.sum()}\")", " ", "+    if A.keep_frac < 1.0:    # ablation only -- costs ~45-90 dev PPL, see claim.md", "+        X = feats[:, [IDX[n] for n in DIST_FEATS]]", "+        dist = np.sqrt((((X - mu) / sd) ** 2).mean(1))", "+        keep &= dist <= np.quantile(dist[keep], A.keep_frac)", "+        print(f\"+ surface-distribution gate keeps {keep.sum()}\")", "+", "     odds = S[:, :4] - S[:, 4:5]                     # log-odds register r vs. raw pool", "     assign, best = odds.argmax(1), odds.max(1)", "     cand = np.flatnonzero(keep)"]}], "userModified": false, "replaceAll": false}

/tmp/nj2000.json: packed 12000000 tok, 7715 docs, mean doc 1555 tok -> /tmp/nj2000_train.npy
/tmp/nj400.json: packed 12000000 tok, 11925 docs, mean doc 1006 tok -> /tmp/nj400_train.npy
/tmp/njqa.json: packed 12000000 tok, 9418 docs, mean doc 1274 tok -> /tmp/njqa_train.npy
---
lightgate HELDOUT_PPL 416.387  loss 6.0316  train_s 56  /tmp/lightgate_train.npy

[stdout]
/tmp/nj2000.json: packed 12000000 tok, 7715 docs, mean doc 1555 tok -> /tmp/nj2000_train.npy
/tmp/nj400.json: packed 12000000 tok, 11925 docs, mean doc 1006 tok -> /tmp/nj400_train.npy
/tmp/njqa.json: packed 12000000 tok, 9418 docs, mean doc 1274 tok -> /tmp/njqa_train.npy
---
lightgate HELDOUT_PPL 416.387  loss 6.0316  train_s 56  /tmp/lightgate_train.npy
[metadata] {"noOutputExpected": false}

nj2000 HELDOUT_PPL 353.029  loss 5.8665  train_s 78  /tmp/nj2000_train.npy
4
nojunkrand HELDOUT_PPL 437.138  loss 6.0802  train_s 77  /tmp/nojunkrand_train.npy
3308

[stdout]
nj2000 HELDOUT_PPL 353.029  loss 5.8665  train_s 78  /tmp/nj2000_train.npy
4
nojunkrand HELDOUT_PPL 437.138  loss 6.0802  train_s 77  /tmp/nojunkrand_train.npy
3308
[metadata] {"noOutputExpected": false}

selected 23176 docs ~26.4M est tokens, dup-dropped 826
register token mix: [0.25 0.25 0.25 0.25]
wrote /tmp/njweb.json
nj2000 HELDOUT_PPL 353.029  loss 5.8665  train_s 78  /tmp/nj2000_train.npy
nj400 HELDOUT_PPL 347.196  loss 5.8499  train_s 56  /tmp/nj400_train.npy

[stdout]
selected 23176 docs ~26.4M est tokens, dup-dropped 826
register token mix: [0.25 0.25 0.25 0.25]
wrote /tmp/njweb.json
nj2000 HELDOUT_PPL 353.029  loss 5.8665  train_s 78  /tmp/nj2000_train.npy
nj400 HELDOUT_PPL 347.196  loss 5.8499  train_s 56  /tmp/nj400_train.npy
[metadata] {"noOutputExpected": false}

register token mix: [0.25 0.25 0.25 0.25]
wrote /tmp/njshuf.json
nj2000 HELDOUT_PPL 353.029  loss 5.8665  train_s 78  /tmp/nj2000_train.npy
nj400 HELDOUT_PPL 347.196  loss 5.8499  train_s 56  /tmp/nj400_train.npy

[stdout]
register token mix: [0.25 0.25 0.25 0.25]
wrote /tmp/njshuf.json
nj2000 HELDOUT_PPL 353.029  loss 5.8665  train_s 78  /tmp/nj2000_train.npy
nj400 HELDOUT_PPL 347.196  loss 5.8499  train_s 56  /tmp/nj400_train.npy
[metadata] {"noOutputExpected": false}

nj400 HELDOUT_PPL 347.196  loss 5.8499  train_s 56  /tmp/nj400_train.npy
njqa HELDOUT_PPL 354.838  loss 5.8717  train_s 57  /tmp/njqa_train.npy
3198

[stdout]
nj400 HELDOUT_PPL 347.196  loss 5.8499  train_s 56  /tmp/nj400_train.npy
njqa HELDOUT_PPL 354.838  loss 5.8717  train_s 57  /tmp/njqa_train.npy
3198
[metadata] {"noOutputExpected": false}

target reference docs: {'wiki': 1546, 'web': 181, 'news': 444, 'qa': 358}
classifier held-out acc: wiki=0.97 web=0.11 news=0.79 qa=0.94 __pool__=0.97
length floor keeps 139147 / 182016
  wiki   33346 docs   44.7M est tokens
  web    47565 docs   56.6M est tokens
  news   40343 docs   42.4M est tokens
  qa     17893 docs   26.3M est tokens
selected 22747 docs, ~26.0M est tokens (834 near-duplicates dropped); register mix [0.25 0.25 0.25 0.25]
wrote /workspace/submission/selection.json
n 22747 overlap first 11000: 1.0
range ok: True unique: True

[stdout]
target reference docs: {'wiki': 1546, 'web': 181, 'news': 444, 'qa': 358}
classifier held-out acc: wiki=0.97 web=0.11 news=0.79 qa=0.94 __pool__=0.97
length floor keeps 139147 / 182016
  wiki   33346 docs   44.7M est tokens
  web    47565 docs   56.6M est tokens
  news   40343 docs   42.4M est tokens
  qa     17893 docs   26.3M est tokens
selected 22747 docs, ~26.0M est tokens (834 near-duplicates dropped); register mix [0.25 0.25 0.25 0.25]
wrote /workspace/submission/selection.json
n 22747 overlap first 11000: 1.0
range ok: True unique: True
[metadata] {"noOutputExpected": false}

/tmp/njweb.json: packed 12000000 tok, 10063 docs, mean doc 1192 tok -> /tmp/njweb_train.npy
/tmp/njshuf.json: packed 12000000 tok, 8284 docs, mean doc 1449 tok -> /tmp/njshuf_train.npy
njweb HELDOUT_PPL 348.848  loss 5.8546  train_s 58  /tmp/njweb_train.npy
njshuf HELDOUT_PPL 444.973  loss 6.0980  train_s 56  /tmp/njshuf_train.npy

[stdout]
/tmp/njweb.json: packed 12000000 tok, 10063 docs, mean doc 1192 tok -> /tmp/njweb_train.npy
/tmp/njshuf.json: packed 12000000 tok, 8284 docs, mean doc 1449 tok -> /tmp/njshuf_train.npy
njweb HELDOUT_PPL 348.848  loss 5.8546  train_s 58  /tmp/njweb_train.npy
njshuf HELDOUT_PPL 444.973  loss 6.0980  train_s 56  /tmp/njshuf_train.npy
[metadata] {"noOutputExpected": false}

packed 12000000 tokens from 22747 ids -> /tmp/dev_train.npy
HELDOUT_PPL 341.255  loss 5.8326  train_s 56  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
3057

[stdout]
packed 12000000 tokens from 22747 ids -> /tmp/dev_train.npy
HELDOUT_PPL 341.255  loss 5.8326  train_s 56  /tmp/dev_train.npy
(dev PPL on multi_dev; official uses a hidden disjoint sample of the same HQ domain)
3057
[metadata] {"noOutputExpected": false}

File created successfully at: /workspace/score_chunks.py (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/workspace/score_chunks.py", "content": "\"\"\"Experiment: rank documents by their register log-odds averaged over THREE 2KB\nchunks (head / 40% / 75%) instead of the head alone, then re-run the balanced\nselection. Writes /tmp/chunk3.json.\"\"\"\nimport sys, json, numpy as np, torch, importlib.util\nsys.argv = [\"curate.py\"]\nspec = importlib.util.spec_from_file_location(\"cur\", \"/workspace/submission/curate.py\")\ncur = importlib.util.module_from_spec(spec); spec.loader.exec_module(cur)\n\nids, feats, prefix = cur.pool_features()\nN = len(ids)\ntg = cur.target_docs()\n\n# --- retrain the same classifier, but keep W so we can score arbitrary chunks\nimport torch.nn.functional as F\ntorch.manual_seed(cur.SEED); rng = np.random.default_rng(cur.SEED)\nneg = cur.hash_ngrams(prefix[rng.choice(N, size=24000, replace=False)])\npos = {r: cur.hash_ngrams(cur.byte_matrix(tg[r])) for r in cur.REGISTERS}\nclasses = cur.REGISTERS + [\"__pool__\"]\nC = len(classes)\ntr = dict(pos); tr[\"__pool__\"] = neg\nW = torch.zeros(cur.NBUCKET + 1, C, device=cur.DEVICE, requires_grad=True)\nbias = torch.zeros(C, device=cur.DEVICE, requires_grad=True)\nopt = torch.optim.Adam([W, bias], lr=0.02)\nfor step in range(1200):\n    xs, ys = [], []\n    for ci, r in enumerate(classes):\n        sel = torch.randint(0, tr[r].shape[0], (128,), device=cur.DEVICE)\n        xs.append(tr[r][sel]); ys.append(torch.full((128,), ci, device=cur.DEVICE))\n    loss = F.cross_entropy(cur.logits_of(W, bias, torch.cat(xs)), torch.cat(ys))\n    opt.zero_grad(set_to_none=True); loss.backward(); opt.step()\nprint(\"trained\", float(loss))\n\n# --- extract three chunks per document\nL = cur.PREFIX\nchunks = np.zeros((N, 3, L), dtype=np.uint8)\npos_of = {int(i): k for k, i in enumerate(ids)}\nfor raw in open(\"/workspace/data/pool.jsonl\", \"rb\"):\n    r = json.loads(raw)\n    b = r[\"text\"].encode(\"utf-8\", \"ignore\")\n    k = pos_of[r[\"id\"]]\n    for j, frac in enumerate((0.0, 0.40, 0.75)):\n        s = min(int(len(b) * frac), max(0, len(b) - L))\n        seg = b[s:s + L]\n        chunks[k, j, :len(seg)] = np.frombuffer(seg, dtype=np.uint8)\nprint(\"chunks extracted\")\n\nwith torch.no_grad():\n    S = np.zeros((N, C), dtype=np.float32)\n    for i in range(0, N, 2048):\n        c = chunks[i:i + 2048]\n        lp = 0\n        for j in range(3):\n            h = cur.hash_ngrams(c[:, j])\n            lp = lp + F.log_softmax(cur.logits_of(W, bias, h), -1)\n        S[i:i + 2048] = (lp / 3).float().cpu().numpy()\nnp.savez(\"/workspace/cache/scores3.npz\", ids=ids, scores=S)\nprint(\"scored\")\n\n# --- same balanced selection as curate.py, on the 3-chunk scores\nsig = np.load(\"/workspace/cache/scores.npz\")[\"sig\"]\nf = lambda n: feats[:, cur.IDX[n]]\ncand = np.flatnonzero(f(\"nchar\") >= 1000)\nodds = S[:, :4] - S[:, 4:5]\nassign, best = odds.argmax(1), odds.max(1)\nest = np.maximum(1, f(\"nchar\") / cur.CHARS_PER_TOK).astype(np.int64)\nlists = []\nfor ci in range(4):\n    sub = cand[assign[cand] == ci]\n    lists.append(list(sub[np.argsort(-best[sub])]))\n    print(cur.REGISTERS[ci], len(sub), round(est[sub].sum() / 1e6, 1), \"M\")\nmix = np.full(4, 0.25)\nseen = set()\ndef fresh(i):\n    bands = [hash(sig[i, b * 8:(b + 1) * 8].tobytes()) for b in range(4)]\n    if any(b in seen for b in bands): return False\n    seen.update(bands); return True\nptr, tok, order = [0] * 4, np.zeros(4), []\nwhile True:\n    avail = [c for c in range(4) if ptr[c] < len(lists[c])]\n    if not avail: break\n    ci = max(avail, key=lambda c: (mix - tok / max(1.0, tok.sum()))[c])\n    i = lists[ci][ptr[ci]]; ptr[ci] += 1\n    if not fresh(i): continue\n    order.append(int(ids[i])); tok[ci] += est[i]\n    if tok.sum() >= 26_000_000: break\nprint(len(order), tok.sum() / 1e6, np.round(tok / tok.sum(), 3))\njson.dump(order, open(\"/tmp/chunk3.json\", \"w\"))\n", "structuredPatch": [], "originalFile": null, "userModified": false}

Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)
  print("trained", float(loss))
trained 0.13387081027030945
chunks extracted
scored
wiki 29732 40.7 M
web 55017 66.8 M
news 36475 37.9 M
qa 17938 25.7 M
21770 26.001023 [0.251 0.25  0.25  0.25 ]

real	0m20.766s
user	0m24.210s
sys	0m2.690s

[stdout]
Consider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)
  print("trained", float(loss))
trained 0.13387081027030945
chunks extracted
scored
wiki 29732 40.7 M
web 55017 66.8 M
news 36475 37.9 M
qa 17938 25.7 M
21770 26.001023 [0.251 0.25  0.25  0.25 ]

real	0m20.766s
user	0m24.210s
sys	0m2.690s
[metadata] {"noOutputExpected": false}

3007

[stdout]
3007
[metadata] {"noOutputExpected": false}

wrote /tmp/njpure.json
/tmp/chunk3.json: packed 12000000 tok, 9990 docs, mean doc 1201 tok -> /tmp/chunk3_train.npy

[stdout]
wrote /tmp/njpure.json
/tmp/chunk3.json: packed 12000000 tok, 9990 docs, mean doc 1201 tok -> /tmp/chunk3_train.npy
[metadata] {"noOutputExpected": false}

CHUNK3 HELDOUT_PPL 352.021  loss 5.8637  train_s 56  /tmp/chunk3_train.npy
2857

[stdout]
CHUNK3 HELDOUT_PPL 352.021  loss 5.8637  train_s 56  /tmp/chunk3_train.npy
2857
[metadata] {"noOutputExpected": false}