| --- |
| language: |
| - en |
| tags: |
| - vision-language-action |
| - gaming-agent |
| - fps |
| - action-chunk |
| - event-driven-control |
| license: other |
| --- |
| |
| # GamePlayer-1.4M |
|
|
| This is the frozen broad multi-game SFT mixture used in the EventChunk-FPS |
| research lineage. |
|
|
| ## Contents |
|
|
| - Train: 1,351,283 conversation rows, 10,677,608 effective assistant turns. |
| - Validation: 73,980 conversation rows, 586,786 effective assistant turns. |
| - Total: 1,425,263 rows and 11,264,394 image/action supervision turns. |
| - 23,509 train and 1,359 validation trajectory groups, with zero reported |
| train/validation trajectory overlap and zero missing image paths. |
| - Action horizons: 8, 12, 16, or 24 atomic actions. |
|
|
| The source data comes from [OpenP2P](https://huggingface.co/elefantai/open-p2p), [CrossFPS](https://huggingface.co/datasets/zizhaotong/CrossFPS-train), [CS2](https://huggingface.co/datasets/RekaAI/CS2-10k), [Gaming500](https://huggingface.co/datasets/markov-ai/gaming-500-hours), and a small ViZDoom |
| success-anchor set we created. You can get the source video from these repo, cause we just provide the frames from codec. See `sft_v16_scale100_mixture.manifest.json` and `validation.json` under `archives/metadata.tar` for exact source and game counts. |
|
|
| ## Format |
|
|
| Rows use ShareGPT-style multi-turn conversations. `images` contains one RGB |
| path per decision; each assistant turn contains a strict action-chunk DSL: |
|
|
| ```text |
| <p>m=... h=... i=...</p> |
| <a>k=... x=... y=... l=... r=... m=...</a> |
| ... |
| <eoc> |
| ``` |
|
|
