Datasets:

TESS-Computer
/

csgo-vla-stage1-5hz

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.

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?

VLA inference speed: 62ms (16Hz) is too fast for current VLMs. 200ms (5Hz) is achievable.
No information loss: Each chunk predicts exactly what the human did for 3 consecutive frames.
World model sync: Diamond executes step(a1), step(a2), step(a3) then returns frame to VLA.

16Hz variant - 1 action per frame
Diamond World Model - For evaluation
Original Dataset