Add cleaned DeepScaleR verl train/val JSON Lines, dataset report and README

README.md

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+# DeepScaleR-Verl-Clean
+
+Cleaned, deduplicated export of the **agentica-org/DeepScaleR-Preview-Dataset**
+(https://huggingface.co/datasets/agentica-org/DeepScaleR-Preview-Dataset), formatted for
+verl-based GRPO training.
+
+## Splits
+
+| Split | File | Rows |
+|---|---|---|
+| Train | `data/train.jsonl` | 1950 |
+| Validation | `data/val.jsonl` | 50 |
+
+## Cleaning
+
+The raw export contained 2100 lines. Lines were rejected in the following order
+(first match wins), leaving 2000 usable rows:
+
+1. **`foreign_source`** — 15 lines whose `data_source` was not `DeepScaleR`
+   (entries that slipped in from the merged pool).
+2. **`missing_field`** — 25 lines whose `answer` or `solution` was absent or blank.
+3. **`duplicate`** — 60 lines whose problem text already appeared on an earlier
+   surviving line; only the first occurrence is kept.
+
+Surviving rows were sorted by problem text (ascending, plain string comparison, ignoring
+surrounding whitespace), numbered 0..1999, and every row whose index is divisible by 40
+was assigned to validation, the rest to train. Row order inside each file is ascending
+`extra_info.index`. Problems, answers and solutions are carried over verbatim from the
+source - no reformatting or re-derivation.
+
+## Schema (verl)
+
+Each JSON Lines row:
+
+```json
+{
+  "data_source": "DeepScaleR",
+  "prompt": [{"role": "user", "content": "<problem text>"}],
+  "ability": "math",
+  "reward_model": {"style": "rule", "ground_truth": "<answer>"},
+  "extra_info": {"index": 0, "solution": "<reference solution>"}
+}
+```
+
+See `dataset_report.json` in this repository for the full accounting of the cleaning run,
+including the rejection counts per reason and the SHA-256 digests of the parquet files that
+back this dataset.

dataset_report.json

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+{
+  "raw_rows": 2100,
+  "kept_rows": 2000,
+  "dropped": {
+    "foreign_source": 15,
+    "missing_field": 25,
+    "duplicate": 60
+  },
+  "train_rows": 1950,
+  "val_rows": 50,
+  "manifest_check": {
+    "discrepancies": [
+      {
+        "field": "valid_unique_problems",
+        "manifest_value": 2050,
+        "observed_value": 2000
+      },
+      {
+        "field": "known_duplicate_entries",
+        "manifest_value": 75,
+        "observed_value": 60
+      }
+    ]
+  },
+  "sha256": {
+    "verl_deepscaler_train.parquet": "e51b4f3dc20da2a8eef9c75797a2df481510e4232983041daa74dc551bd529fa",
+    "verl_deepscaler_val.parquet": "83e0271fe9c292a4de453f11ea7936f081796503ed0986649cea3e46e65cc348"
+  }
+}