Tsinghua Spatial Intelligence Lab

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GameScaling-0.8B-S3

Tsinghua Spatial Intelligence Lab

This repository: GameScaling-0.8B-S3. Qwen3.5-0.8B continued pre-trained on a balanced mixture of 27% Minecraft and other games (S3), 1,600 h of gameplay, 6,760 training steps; 2.2 GB of bfloat16 weights on 1 GPU. Serve it with bash scripts/servevllm.sh THUSI-Lab/GameScaling-0.8B-S3 8000 (§1). The inference client, prompts and scripts are in THUSI-Lab/GameScaling; all eight checkpoints are in the GameScaling models are Qwen3.5 vision-language models with continued pre-training on keyboard-and-mouse gameplay. Each call takes the current game frame (plus a short history of past frames and actions) and returns the next 200 ms of keyboard and mouse actions: 6 steps of 33 ms each, plus one mouse…

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GameScaling-9B-S3

Tsinghua Spatial Intelligence Lab

This repository: GameScaling-9B-S3. Qwen3.5-9B continued pre-trained on a balanced mixture of 27% Minecraft and other games (S3), 1,600 h of gameplay, 6,760 training steps; 18.8 GB of bfloat16 weights on 1 GPU. Serve it with bash scripts/servevllm.sh THUSI-Lab/GameScaling-9B-S3 8000 (§1). The inference client, prompts and scripts are in THUSI-Lab/GameScaling; all eight checkpoints are in the GameScaling models are Qwen3.5 vision-language models with continued pre-training on keyboard-and-mouse gameplay. Each call takes the current game frame (plus a short history of past frames and actions) and returns the next 200 ms of keyboard and mouse actions: 6 steps of 33 ms each, plus one mouse…

Model · Image and text to text

GameScaling-2B-S3

Tsinghua Spatial Intelligence Lab

This repository: GameScaling-2B-S3. Qwen3.5-2B continued pre-trained on a balanced mixture of 27% Minecraft and other games (S3), 1,600 h of gameplay, 6,760 training steps; 5.4 GB of bfloat16 weights on 1 GPU. Serve it with bash scripts/servevllm.sh THUSI-Lab/GameScaling-2B-S3 8000 (§1). The inference client, prompts and scripts are in THUSI-Lab/GameScaling; all eight checkpoints are in the GameScaling models are Qwen3.5 vision-language models with continued pre-training on keyboard-and-mouse gameplay. Each call takes the current game frame (plus a short history of past frames and actions) and returns the next 200 ms of keyboard and mouse actions: 6 steps of 33 ms each, plus one mouse…

Model · Image and text to text

GameScaling-0.8B-S1

Tsinghua Spatial Intelligence Lab

This repository: GameScaling-0.8B-S1. Qwen3.5-0.8B continued pre-trained on Minecraft gameplay only (S1), 1,600 h of gameplay, 6,736 training steps; 2.2 GB of bfloat16 weights on 1 GPU. Serve it with bash scripts/servevllm.sh THUSI-Lab/GameScaling-0.8B-S1 8000 (§1). The inference client, prompts and scripts are in THUSI-Lab/GameScaling; all eight checkpoints are in the GameScaling models are Qwen3.5 vision-language models with continued pre-training on keyboard-and-mouse gameplay. Each call takes the current game frame (plus a short history of past frames and actions) and returns the next 200 ms of keyboard and mouse actions: 6 steps of 33 ms each, plus one mouse movement and scroll for the…

Model · Image and text to text

GameScaling-2B-S1

Tsinghua Spatial Intelligence Lab

This repository: GameScaling-2B-S1. Qwen3.5-2B continued pre-trained on Minecraft gameplay only (S1), 1,600 h of gameplay, 6,736 training steps; 5.4 GB of bfloat16 weights on 1 GPU. Serve it with bash scripts/servevllm.sh THUSI-Lab/GameScaling-2B-S1 8000 (§1). The inference client, prompts and scripts are in THUSI-Lab/GameScaling; all eight checkpoints are in the GameScaling models are Qwen3.5 vision-language models with continued pre-training on keyboard-and-mouse gameplay. Each call takes the current game frame (plus a short history of past frames and actions) and returns the next 200 ms of keyboard and mouse actions: 6 steps of 33 ms each, plus one mouse movement and scroll for the whole…

Model · Image and text to text

GameScaling-27B-S3

Tsinghua Spatial Intelligence Lab

This repository: GameScaling-27B-S3. Qwen3.5-27B continued pre-trained on a balanced mixture of 27% Minecraft and other games (S3), 1,600 h of gameplay, 7,020 training steps; 54.7 GB of bfloat16 weights on 2 GPUs (tensor parallel). Serve it with bash scripts/servevllm.sh THUSI-Lab/GameScaling-27B-S3 8000 2 (§1). The inference client, prompts and scripts are in THUSI-Lab/GameScaling; all eight checkpoints are in the GameScaling models are Qwen3.5 vision-language models with continued pre-training on keyboard-and-mouse gameplay. Each call takes the current game frame (plus a short history of past frames and actions) and returns the next 200 ms of keyboard and mouse actions: 6 steps of 33 ms…

Model · Image and text to text

GameScaling-9B-S1

Tsinghua Spatial Intelligence Lab

This repository: GameScaling-9B-S1. Qwen3.5-9B continued pre-trained on Minecraft gameplay only (S1), 1,600 h of gameplay, 6,736 training steps; 18.8 GB of bfloat16 weights on 1 GPU. Serve it with bash scripts/servevllm.sh THUSI-Lab/GameScaling-9B-S1 8000 (§1). The inference client, prompts and scripts are in THUSI-Lab/GameScaling; all eight checkpoints are in the GameScaling models are Qwen3.5 vision-language models with continued pre-training on keyboard-and-mouse gameplay. Each call takes the current game frame (plus a short history of past frames and actions) and returns the next 200 ms of keyboard and mouse actions: 6 steps of 33 ms each, plus one mouse movement and scroll for the…

Model · Image and text to text

GameScaling-27B-S1

Tsinghua Spatial Intelligence Lab

This repository: GameScaling-27B-S1. Qwen3.5-27B continued pre-trained on Minecraft gameplay only (S1), 1,600 h of gameplay, 6,995 training steps; 54.7 GB of bfloat16 weights on 2 GPUs (tensor parallel). Serve it with bash scripts/servevllm.sh THUSI-Lab/GameScaling-27B-S1 8000 2 (§1). The inference client, prompts and scripts are in THUSI-Lab/GameScaling; all eight checkpoints are in the GameScaling models are Qwen3.5 vision-language models with continued pre-training on keyboard-and-mouse gameplay. Each call takes the current game frame (plus a short history of past frames and actions) and returns the next 200 ms of keyboard and mouse actions: 6 steps of 33 ms each, plus one mouse movement…

Model · Image and text to text

GameScaling

Tsinghua Spatial Intelligence Lab

GameScaling models are Qwen3.5 vision-language models with continued pre-training on keyboard-and-mouse gameplay. Each call takes the current game frame (plus a short history of past frames and actions) and returns the next 200 ms of keyboard and mouse actions: 6 steps of 33 ms each, plus one mouse movement and scroll for the whole chunk. The model may think before it acts. This repository contains the inference client and the standard system-prompt template; each of the eight checkpoints is its own repository (below, and in the GameScaling collection). Together they are everything needed to serve a checkpoint and run it in a game; there is no training code. Paper results and analysis are…