game2player/Game2Player-4B

Game2Player 4B

Game2Player 4B is a visual game-playing model fine-tuned from Qwen3.5-4B. It takes game screenshots, a task description and recent actions as input, then predicts the next keyboard or mouse action.

This repository contains the complete model weights. Load it directly with vLLM or Transformers.

Game2Player: Scaling Gameplay Experience for Visual Agents through Environment–Player Co-Development

Project · Code · PlayerBench

Training

The model was trained on gameplay collected from code policies and VLM players with Game2Player. Training updates the language model while keeping the vision encoder and visual projector frozen.

Setting	Value
Training examples	1,564,236
Training duration	One epoch, 9,237 optimizer steps
Maximum training sequence length	25,000 tokens
Learning rate	0.00001 with a cosine schedule
Weight precision	BF16

Dataset coming soon.

Input and output

Provide the game rules, available controls and task goal in the system message. The user message contains the current screenshot and, when available, up to 50 previous actions with their short notes and six previous screenshots. Use at most seven images in total and about 1,024 visual tokens per image.

The model returns a computer_use action inside <tool_call> tags. For example:

<tool_call>
{"name":"computer_use","arguments":{"action":"press_key","key":"ArrowRight","reasoning":"Move toward the exit."}}
</tool_call>

The reasoning field contains a short action note. Longer thinking, when enabled, is returned separately. Mouse coordinates use a normalized 0–1000 range and action durations are in seconds.

Run the model

Install vLLM and start an OpenAI-compatible server:

pip install vllm==0.29.0 requests

vllm serve game2player/Game2Player-4B \
  --served-model-name game2player-4b \
  --dtype bfloat16 \
  --max-model-len 65536 \
  --max-num-seqs 8 \
  --max-num-batched-tokens 4096 \
  --limit-mm-per-prompt '{"image":7,"video":0}' \
  --mm-processor-kwargs '{"max_pixels":1048576}' \
  --reasoning-parser qwen3

Save a screenshot as screen.png and send a request. Replace the example game rules and task with those of the game you want to play.

import base64
from pathlib import Path
import requests

system_prompt = """You are an expert game agent. Choose the next action from the screenshot.

# Game Rules
Move the character through the maze to reach the exit. Walls block movement.

# Role and Controls
ArrowUp, ArrowDown, ArrowLeft and ArrowRight move the character.

# Task Instruction
Reach the exit.

# Output Format
Return exactly one action inside <tool_call> tags.
Use press_key with a key from the controls above, or wait with a duration in seconds.
Include reasoning with one short sentence explaining the action.
Think in the thinking channel, then emit only the tool call as the final answer.
Example:
<tool_call>{"name":"computer_use","arguments":{"action":"press_key","key":"ArrowRight","reasoning":"Move toward the exit."}}</tool_call>
"""

encoded = base64.b64encode(Path("screen.png").read_bytes()).decode("ascii")
response = requests.post(
    "http://localhost:8000/v1/chat/completions",
    json={
        "model": "game2player-4b",
        "messages": [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": [
                {"type": "text", "text": "# Current step\nCurrent game screen:\n"},
                {"type": "image_url", "image_url": {
                    "url": "data:image/png;base64," + encoded
                }},
                {"type": "text", "text": "\nReturn the next action."},
            ]},
        ],
        "temperature": 1.0,
        "max_tokens": 512,
        "chat_template_kwargs": {"enable_thinking": False},
    },
    timeout=180,
)
response.raise_for_status()
print(response.json()["choices"][0]["message"]["content"])

To enable thinking, set enable_thinking to True and increase max_tokens and the request timeout. Our thinking evaluations allow up to 32,768 output tokens. Keep previous action notes in the history and omit previous thinking chains.

Evaluation

We evaluate Game2Player on PlayerBench, which contains 50 held-out games and 287 tasks, and GameWorld, which contains 34 games and 170 tasks. Results are available on the project leaderboard.

License

The model weights are released under Apache-2.0. Qwen3.5 is also licensed under Apache-2.0. Game titles, assets and screenshots belong to their respective owners. The model license does not grant rights to third-party game content.