Document raw SPARC use and release filtering

README.md

@@ -13,11 +13,23 @@ This repository contains the unfiltered SPARC VQA corpus: 838,211 embedded-image
 
 ## Ready-to-train version
 
-For the exact SPARC subset used by the released Qwen3.5 models, use [irl-kit/SPARC-VQA](https://huggingface.co/datasets/irl-kit/SPARC-VQA). Its `train_filtered_t097_mpo700.parquet` contains 284,909 examples after the release filtering and does not require SPARC postprocessing.
+For the exact processed SPARC subset used by the released Qwen3.5 models, download `train_filtered_t097_mpo700.parquet` from [irl-kit/SPARC-VQA](https://huggingface.co/datasets/irl-kit/SPARC-VQA). It contains 284,909 examples and can be used directly without SPARC postprocessing.
+
+After download, load it as a Parquet dataset:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset(
+  "parquet",
+  data_files="train_filtered_t097_mpo700.parquet",
+  split="train",
+)
+```
 
 ## Reproduce the release filter
 
-`export_sparc_training_subset.py` materializes the exact release subset from `train.parquet`:
+To create the same subset from this unfiltered corpus, download `train.parquet` and run the included `export_sparc_training_subset.py` in the same directory:
 
 ```bash
 python export_sparc_training_subset.py \
@@ -30,6 +42,8 @@ python export_sparc_training_subset.py \
 
 The filter applies `selected_start_score >= 0.97`, sorts retained records by descending score, retains complete trajectory-subtask groups for the first 700 groups per normalized object phrase, and removes vacant-location prompts referring to a gripper.
 
+For a custom raw-data subset, change any of `--quality-threshold`, `--max-per-object`, or `--blocked-vacant-location`, then load the resulting Parquet with the same `load_dataset("parquet", ...)` call above. The output preserves the embedded Hugging Face-compatible image column.
+
 ## Data schema
 
 | Field | Description |
@@ -41,7 +55,7 @@ The filter applies `selected_start_score >= 0.97`, sorts retained records by des
 | `target_type`, `task_type` | Spatial target and VQA task labels |
 | `source`, `split`, `metadata` | Provenance and generation metadata |
 
-`ours_adaptive_det_soft_snr_sp8.yaml` records the raw SPARC VQA generation configuration.
+`ours_adaptive_det_soft_snr_sp8.yaml` records the raw SPARC VQA generation configuration. `release_mixture.yaml` records the SPARC and external-dataset mixture used for each released model.
 
 ## Citation