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

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. 1.VLA inference speed: 62ms (16Hz) is too fast for current VLMs. 200ms (5Hz) is achievable.
2. 2.No information loss: Each chunk predicts exactly what the human did for 3 consecutive frames.
3. 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