Datasets:

aabbdev
/

StateBench-v1

Remove model-visible benchmark labels, fix multi-field JSON templates, remove empty fields, neutralize pretraining role markers, and consolidate shards.

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README.md +4 -2
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README.md CHANGED Viewed

@@ -33,7 +33,7 @@ One deterministic corpus. Three training views: **pretraining · SFT · on-polic
 | Scale | Coverage | Storage | Splits |
 |:--|:--|:--|:--|
-| **10,000,000 rows** | **36 families · M1–M11** | **8.09 GiB · 800 Parquet shards** | **9,949,600 train · 50,400 validation** |
 StateBench teaches models to execute finite state programs rather than recall facts. A typed program is executed by a deterministic reference evaluator; only then is it rendered into varied textual surfaces.
@@ -88,4 +88,6 @@ Difficulty is balanced across levels 0, 2, 4, 5, and 6. Family allocation differ
 - dataset_manifest.json records generation parameters, distributions, shard sizes, and SHA-256 checksums.
 - The public corpus is training data, not the private StateBench evaluation split.
-Generated with **StateBench-v1.0**. The canonical dataset contains both SFT and SDFT fields so no semantic example is duplicated between training formats.

 | Scale | Coverage | Storage | Splits |
 |:--|:--|:--|:--|
+| **10,000,000 rows** | **36 families · M1–M11** | **7.80 GiB · 41 Parquet shards** | **9,949,600 train · 50,400 validation** |
 StateBench teaches models to execute finite state programs rather than recall facts. A typed program is executed by a deterministic reference evaluator; only then is it rendered into varied textual surfaces.
 - dataset_manifest.json records generation parameters, distributions, shard sizes, and SHA-256 checksums.
 - The public corpus is training data, not the private StateBench evaluation split.
+Generated with **StateBench-v1.1**. The canonical dataset contains both SFT and SDFT fields so no semantic example is duplicated between training formats.
+The v1.1 data revision removes benchmark/family labels from model-visible prompts, emits one valid JSON output template for multi-field answers, and omits empty semantic fields. For the longest examples, configure the trainer context explicitly; the provided SFT/SDFT scripts default to conservative 17,408/16,384-token limits.

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