Apache-2.0 license; model card with badges, affiliations, teaser and results

.gitattributes

@@ -41,3 +41,4 @@ models/2b_s1_1600h_gs6736/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 models/2b_s3_1600h_gs6760/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 models/9b_s1_1600h_gs6736/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 models/9b_s3_1600h_gs6760/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+assets/teaser.jpg filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1,202 @@
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README.md

@@ -1,17 +1,63 @@
 ---
+license: apache-2.0
 library_name: transformers
 pipeline_tag: image-text-to-text
+base_model:
+- Qwen/Qwen3.5-0.8B
+- Qwen/Qwen3.5-2B
+- Qwen/Qwen3.5-9B
+- Qwen/Qwen3.5-27B
+base_model_relation: finetune
+language:
+- en
 tags:
 - gamescaling
 - game-agent
 - embodied-agent
 - keyboard-mouse
+- continued-pretraining
 - qwen3_5
 ---
 
-# Scaling in Games: Continued Pre-Training for Embodied Agents in Diverse Virtual Worlds
+<div align="center">
 
-[Project page](https://gamescaling.github.io) · [Citation](#citation)
+# Scaling in Games
+
+### Continued Pre-Training for Embodied Agents in Diverse Virtual Worlds
+
+**Kuan Zhang**<sup>1,2,\*,‡</sup>, **Yukun Chen**<sup>1,3,\*,‡</sup>, **Zhihao Yang**<sup>1,3,\*,‡</sup>, Yue Su<sup>4</sup>, Xiangnan Wu<sup>3</sup>, Zirong Chen<sup>2</sup>,<br>
+Run Luo<sup>1,5,‡</sup>, Jinkun Hou<sup>6</sup>, Tao Tan<sup>1</sup>, **Yinhe Zheng**<sup>1,†</sup>, **Yiming Li**<sup>2,†</sup>
+
+<sup>1</sup>miHoYo Honkai AI R&D Team &nbsp; <sup>2</sup>College AI, Tsinghua University &nbsp; <sup>3</sup>University of Chinese Academy of Sciences<br>
+<sup>4</sup>MMLab, The University of Hong Kong &nbsp; <sup>5</sup>National University of Singapore &nbsp; <sup>6</sup>Peking University
+
+<sup>\*</sup>Equal contribution &nbsp; <sup>†</sup>Corresponding author &nbsp; <sup>‡</sup>Work done while interning at miHoYo
+
+<p>
+  <img src="https://huggingface.co/THUSI-Lab/GameScaling/resolve/main/assets/logos/mihoyo.svg" alt="miHoYo" height="34">&nbsp;&nbsp;&nbsp;
+  <img src="https://huggingface.co/THUSI-Lab/GameScaling/resolve/main/assets/logos/tsinghua.svg" alt="Tsinghua University" height="40">&nbsp;&nbsp;&nbsp;
+  <img src="https://huggingface.co/THUSI-Lab/GameScaling/resolve/main/assets/logos/ucas.svg" alt="UCAS" height="40">&nbsp;&nbsp;&nbsp;
+  <img src="https://huggingface.co/THUSI-Lab/GameScaling/resolve/main/assets/logos/hku.svg" alt="HKU" height="40">
+</p>
+
+<p>
+  <a href="https://gamescaling.github.io"><img src="https://img.shields.io/badge/Project-Page-1f6feb?style=for-the-badge&logo=googlechrome&logoColor=white" alt="Project Page"></a>
+  <a href="https://gamescaling.github.io/assets/paper.pdf"><img src="https://img.shields.io/badge/Paper-PDF-b31b1b?style=for-the-badge&logo=adobeacrobatreader&logoColor=white" alt="Paper"></a>
+  <a href="https://gamescaling.github.io"><img src="https://img.shields.io/badge/arXiv-coming%20soon-lightgrey?style=for-the-badge&logo=arxiv&logoColor=white" alt="arXiv (coming soon)"></a>
+  <a href="https://gamescaling.github.io"><img src="https://img.shields.io/badge/Code-coming%20soon-lightgrey?style=for-the-badge&logo=github&logoColor=white" alt="Code (coming soon)"></a>
+  <a href="https://huggingface.co/THUSI-Lab/GameScaling"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Models-THUSI--Lab%2FGameScaling-ffcc4d?style=for-the-badge" alt="Models"></a>
+  <a href="https://www.apache.org/licenses/LICENSE-2.0"><img src="https://img.shields.io/badge/License-Apache%202.0-2ea44f?style=for-the-badge" alt="License: Apache 2.0"></a>
+</p>
+
+</div>
+
+<p align="center">
+  <img src="https://huggingface.co/THUSI-Lab/GameScaling/resolve/main/assets/teaser.jpg" alt="From one physical world to diverse virtual worlds" width="100%">
+</p>
+
+<p align="center"><i>Embodied learning in the physical world benefits from shared physics, visuals and controls. Virtual worlds break that assumption: every game brings its own. Does scaling still carry over?</i></p>
+
+## Overview
 
 GameScaling models are Qwen3.5 vision-language models with continued pre-training (CPT) on
 keyboard-and-mouse gameplay. Each call reads the current game frame (plus a short history of past
@@ -19,8 +65,49 @@ frames and actions) and outputs the next **200 ms of keyboard and mouse actions*
 33 ms each, plus one mouse movement and scroll for the whole chunk. The model can optionally
 think first.
 
-This repository releases eight checkpoints (four model sizes × two data mixtures, all trained on
-1,600 h of gameplay), the inference client, and the standard system-prompt template.
+The paper is a controlled study of CPT along three axes: data volume, data diversity and model
+size. It covers 120 training configurations: four Qwen3.5 backbones (0.8B–27B), six gameplay
+budgets (50–1,600 h) and five data mixtures (S1–S5) from an 80-game, mostly 3D corpus. Evaluation
+moves from worlds in the training corpus (MCU / Minecraft, ViZDoom) to unseen related 3D worlds
+(OmniGameArena) and unseen distant, mostly 2D worlds (GameWorld).
+
+This repository releases eight checkpoints (four model sizes × two mixtures, all at 1,600 h), the
+inference client, and the standard system-prompt template.
+
+**Key findings**
+
+- **Data, diversity and size help different worlds.** More data improves play in worlds from the
+  training corpus. Diversity improves transfer to related unseen worlds, with limited gains in
+  distant ones.
+- **Small and fast wins in real time.** GameScaling takes 0.13–0.69 s per step, versus 4–24 s for
+  frontier models, and scores highest when the world does not pause.
+- **More steps help, but only after pre-training.** Raising the MCU step cap from 600 to 3,000
+  lifts GameScaling-9B-S1 from 50.9% to 69.3%; the untrained Qwen3.5-9B stays at 4.0%.
+
+## Results
+
+Average time-paused score (%) of the released 1,600 h checkpoints, as reported on the
+[project page](https://gamescaling.github.io).
+
+| Size | Model | MCU | ViZDoom | OmniGameArena | GameWorld |
+|---|---|:---:|:---:|:---:|:---:|
+| 0.8B | Qwen3.5 (no CPT) | 0.0 | 0.9 | 1.7 | 13.8 |
+| | GameScaling-S1 | **44.0** | 5.4 | 7.1 | 14.4 |
+| | GameScaling-S3 | 36.0 | **11.9** | **10.2** | **20.3** |
+| 2B | Qwen3.5 (no CPT) | 0.0 | 0.9 | 1.8 | **19.8** |
+| | GameScaling-S1 | **49.3** | 10.7 | 5.9 | 15.1 |
+| | GameScaling-S3 | 48.0 | **11.2** | **13.9** | 19.5 |
+| 9B | Qwen3.5 (no CPT) | 3.3 | 2.8 | 5.2 | **29.2** |
+| | GameScaling-S1 | **54.7** | 8.1 | 6.0 | 17.0 |
+| | GameScaling-S3 | 44.7 | **15.9** | **27.3** | 26.2 |
+| 27B | Qwen3.5 (no CPT) | 9.3 | 5.6 | 9.7 | **29.3** |
+| | GameScaling-S1 | **63.3** | 5.3 | 7.9 | 12.5 |
+| | GameScaling-S3 | 54.0 | **14.7** | **25.7** | 25.3 |
+
+S1 is the specialist: it is best on Minecraft (MCU). S3 trades some Minecraft score for clear
+gains in unseen 3D worlds (OmniGameArena). Neither closes the gap in distant 2D worlds (GameWorld),
+which suggests that CPT alone is not enough for a generalist agent. Base and S3 numbers come from
+the paper's tables; S1 numbers are read from the 1,600 h column of the paper's Figure 4.
 
 ## Checkpoints
 
@@ -59,6 +146,8 @@ gamescaling/                 inference client; depends only on requests + Pillow
   keymaps.py                 training key vocabulary <-> browser / Playwright key names
 scripts/serve_vllm.sh        OpenAI-compatible server
 tests/test_core.py           offline unit tests (no GPU or server needed)
+assets/                      teaser and institution logos used in this card
+LICENSE                      Apache License 2.0
 ```
 
 ## 1. Download and serve
@@ -283,6 +372,13 @@ python tests/test_core.py      # or: python -m pytest tests/
 
 Offline checks of prompts, request bodies and action parsing; no GPU or server needed.
 
+## License
+
+The weights and code in this repository are released under the
+[Apache License 2.0](LICENSE). The models are continued pre-trained from
+[Qwen3.5](https://huggingface.co/Qwen/Qwen3.5-9B), which is also Apache 2.0. Game titles, footage
+and other game assets referenced here belong to their respective owners.
+
 ## Citation
 
 ```bibtex