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Document verified SQL queries and reproducible dataset revision

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  1. README.md +10 -6
  2. SHA256SUMS.txt +1 -1
README.md CHANGED
@@ -63,13 +63,13 @@ Numeric columns support the native viewer's distribution charts and value filter
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  ### Load a configuration reproducibly
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- For the aggregate views, copy the current commit hash from [Files and versions](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/tree/main) after checking that the revision includes `model-summary.jsonl`, `family-summary.jsonl` and `completion-depths.jsonl`. Passing that hash records exactly which view you used.
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  ```python
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  from datasets import load_dataset
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  repo = "EnvLoop/Recursive-Play-Bench"
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- revision = input("Dataset commit SHA from Files and versions: ").strip()
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  models = load_dataset(repo, name="models", split="test", revision=revision)
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  families = load_dataset(repo, name="families", split="test", revision=revision)
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  depths = load_dataset(repo, name="depths", split="test", revision=revision)
@@ -89,25 +89,29 @@ The `test` label organises these exposed research records. It does not turn them
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  ### Query an aggregate in Data Studio
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- Open `models/test`, then open its SQL Console and check the table name shown in the console. The example below assumes that the selected view is named `test`. If the console shows a different name, replace `test` in the query with that name before running it.
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  ```sql
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  SELECT primaryRank, displayName, fullWins, fullWinRatePercent,
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  levelsCompleted, levelClearRatePercent
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- FROM test
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  ORDER BY displayOrder;
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  ```
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- To inspect the challenge table, select `challenges/test` and check its view name in the SQL Console. This query uses `test` for that selected view and reproduces the paper's challenge ordering:
 
 
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  ```sql
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  SELECT environmentId, family, maximumCompletedLevelsAcrossEleven,
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  astraCompletedLevels, referenceTotalActions
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- FROM test
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  ORDER BY maximumCompletedLevelsAcrossEleven,
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  referenceTotalActions, environmentId;
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  ```
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  The [native SQL Console](https://huggingface.co/docs/hub/main/en/datasets-viewer-sql-console) can share query results and export tables. The [Data Studio guide](https://huggingface.co/docs/hub/en/data-studio) explains filtering, search, missing-value distributions and row sharing. These facilities inspect the recorded outcomes; they do not run a solver or create a new benchmark score.
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  ## Files and configurations
 
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  ### Load a configuration reproducibly
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+ The aggregate views are available at the recorded commit `e519f64ccc67fb4fc32a393b9f3a77597432336c`. Passing this hash loads the published summary tables reproducibly. Later card-only revisions do not change the data at this commit. You can inspect other revisions under [Files and versions](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/tree/main).
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  ```python
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  from datasets import load_dataset
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  repo = "EnvLoop/Recursive-Play-Bench"
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+ revision = "e519f64ccc67fb4fc32a393b9f3a77597432336c"
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  models = load_dataset(repo, name="models", split="test", revision=revision)
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  families = load_dataset(repo, name="families", split="test", revision=revision)
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  depths = load_dataset(repo, name="depths", split="test", revision=revision)
 
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  ### Query an aggregate in Data Studio
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+ Open [models/test](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/viewer/models/test), then open its SQL Console. Hugging Face registers this selected view as `models`, which is the table name used below. The query follows the paper's recorded display order and retains tied ranks.
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  ```sql
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  SELECT primaryRank, displayName, fullWins, fullWinRatePercent,
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  levelsCompleted, levelClearRatePercent
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+ FROM models
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  ORDER BY displayOrder;
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  ```
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+ Open the saved [Ranking and Model Performance query](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/sql-console/aul_Tu_) to inspect the same eleven rows directly in the native SQL Console.
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+ The SQL Console also lists the challenge view as `challenges`. This query reproduces the paper's challenge ordering using the task-level completion and reference-action fields:
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  ```sql
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  SELECT environmentId, family, maximumCompletedLevelsAcrossEleven,
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  astraCompletedLevels, referenceTotalActions
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+ FROM challenges
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  ORDER BY maximumCompletedLevelsAcrossEleven,
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  referenceTotalActions, environmentId;
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  ```
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+ The saved [Challenge Levels and Actions query](https://huggingface.co/datasets/EnvLoop/Recursive-Play-Bench/sql-console/jGND0-1) opens these fifteen challenge rows in the same order.
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  The [native SQL Console](https://huggingface.co/docs/hub/main/en/datasets-viewer-sql-console) can share query results and export tables. The [Data Studio guide](https://huggingface.co/docs/hub/en/data-studio) explains filtering, search, missing-value distributions and row sharing. These facilities inspect the recorded outcomes; they do not run a solver or create a new benchmark score.
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  ## Files and configurations
SHA256SUMS.txt CHANGED
@@ -1,4 +1,4 @@
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- da4491c4a753e18e7dca339afcabe488868e7932db1f6a9af341b33266f03b83 README.md
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  362ae6fca185ef2e595653558c4f0f96bf7e95585c5152d20df75adedd070031 challenge-inventory.json
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  89e64b2b1b2e43fedd2c984ff70e41c1048bbcc0eb959f3c868dc9eeccaea104 challenge-tasks.jsonl
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  5261f6c28b14ebd3e9a5a10139729c717b2792d24ab363713bd3381220273696 completion-depths.jsonl
 
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+ f7b578e359312c2603aacfb886701224c507695183fc06d7aedf426b9d04f330 README.md
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  362ae6fca185ef2e595653558c4f0f96bf7e95585c5152d20df75adedd070031 challenge-inventory.json
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  89e64b2b1b2e43fedd2c984ff70e41c1048bbcc0eb959f3c868dc9eeccaea104 challenge-tasks.jsonl
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  5261f6c28b14ebd3e9a5a10139729c717b2792d24ab363713bd3381220273696 completion-depths.jsonl