TESS-Computer/csgo-vla-stage1-5hz

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.

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 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

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 - 1 action per frame
• Diamond World Model - For evaluation
• Original Dataset