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README.md

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+---
+license: mit
+task_categories:
+  - robotics
+  - image-to-text
+tags:
+  - VLA
+  - gaming
+  - counter-strike
+  - behavioral-cloning
+  - imitation-learning
+  - action-chunking
+size_categories:
+  - 1M<n<10M
+---
+
+# CS:GO VLA Stage 1 Dataset (5Hz Chunked)
+
+Vision-Language-Action dataset for Counter-Strike: Global Offensive with action chunking, converted from the [TeaPearce CS:GO dataset](https://huggingface.co/datasets/TeaPearce/CounterStrike_Deathmatch).
+
+## Overview
+
+- **Frame rate:** 5Hz (every 3rd frame)
+- **Action chunking:** 3 actions per sample (~200ms coverage)
+- **Total samples:** ~1.8M chunks
+- **Split:** train / test following [Diamond](https://github.com/eloialonso/diamond) split
+- **Map:** Dust2 deathmatch
+
+## Action Format
+
+```
+<|action_start|> m1_x m1_y [keys1] ; m2_x m2_y [keys2] ; m3_x m3_y [keys3] <|action_end|>
+```
+
+**Examples:**
+```
+<|action_start|> 0 0 ; 0 0 ; 0 0 <|action_end|>                              # idle
+<|action_start|> 5 0 W ; 3 0 W ; 4 0 W <|action_end|>                        # walking
+<|action_start|> -200 50 W L ; -50 10 L ; 10 0 W <|action_end|>              # flick shot
+```
+
+Each chunk contains the exact mouse delta and keys for that frame - no aggregation.
+
+## Schema
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `id` | string | Unique sample ID |
+| `episode_id` | string | Source HDF5 file |
+| `chunk_idx` | int32 | Chunk number within episode |
+| `frame_idx` | int32 | Starting frame number |
+| `action` | string | Text-formatted 3-action chunk |
+| `kill_flag` | int32 | 1 if any kill in chunk |
+| `death_flag` | int32 | 1 if any death in chunk |
+| `split` | string | "train" or "test" |
+| `image_bytes` | bytes | JPEG screenshot (first frame) |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load full dataset
+ds = load_dataset("TESS-Computer/csgo-vla-stage1-5hz")
+
+# Filter by split
+train_ds = ds.filter(lambda x: x['split'] == 'train')
+test_ds = ds.filter(lambda x: x['split'] == 'test')
+```
+
+## Why 5Hz with Chunking?
+
+1. **VLA inference speed:** 62ms (16Hz) is too fast for current VLMs. 200ms (5Hz) is achievable.
+2. **No information loss:** Each chunk predicts exactly what the human did for 3 consecutive frames.
+3. **World model sync:** Diamond executes `step(a1), step(a2), step(a3)` then returns frame to VLA.
+
+## Related
+
+- [16Hz variant](https://huggingface.co/datasets/TESS-Computer/csgo-vla-stage1-16hz) - 1 action per frame
+- [Diamond World Model](https://github.com/eloialonso/diamond) - For evaluation
+- [Original Dataset](https://huggingface.co/datasets/TeaPearce/CounterStrike_Deathmatch)