| # Sources and credits |
|
|
| WeightProgramBench packages the existing MinAtar Synth v4 task corpus without |
| changing its game implementations, prompts, parser, or scoring contract. Its |
| 322 tasks comprise 51 Atari-style synthetic training games, 266 Crisp-inspired |
| synthetic training games, and five original MinAtar held-out games. These are |
| compact Python adaptations, not Atari ROM emulation or native Crisp gameplay. |
|
|
| [`TASK_ORIGINS.json`](TASK_ORIGINS.json) records every task's family, source, |
| revision or package version, source digest, and prompt digest. It also retains |
| the exact pinned native-source link from each Crisp conversion's documentation. |
|
|
| ## MinAtar Synth implementations |
|
|
| Credit Trajectory Labs, Jerry Chan, and the contributors to |
| [MinAtar Synth](https://github.com/Trajectorylabs/minatar-synth/tree/4038830175c4201ca10e004ed4441985f2acddf2). |
| The 317 Python training environments are pinned to commit |
| `4038830175c4201ca10e004ed4441985f2acddf2`. The five original MinAtar environments |
| are a separate dependency. The extra `pong.py` in the source repository is not |
| part of this benchmark's task roster. |
|
|
| The pinned MinAtar Synth repository does not declare a license. No blanket license |
| is asserted for the converted implementations; the notices below describe the |
| respective upstream components and do not supply that missing declaration. |
|
|
| The 51 Atari-style conversions document mechanics and deliberate simplifications |
| from historical Atari 2600 games and their manuals. Their documentation's |
| historical-source notes are retained per task in `TASK_ORIGINS.json`. They use |
| NumPy and the Python standard library, not Atari ROMs or the original MinAtar |
| game modules. This describes their implementation and provenance, not a |
| conclusion about the license of the converted code. |
|
|
| ## Crisp game designs and source |
|
|
| Credit **ABA Games** and the authors and contributors to its Crisp games, and |
| **Joyrider3774** and contributors for the portable SDL/C versions. The 266 |
| conversions cite three pinned source collections: |
|
|
| | Source | Conversions | Pinned revision | |
| | --- | ---: | --- | |
| | [crisp-game-lib-games](https://github.com/abagames/crisp-game-lib-games) | 136 | `cfb39d2f988feb5918eb83697145b5a35adabf58` | |
| | [crisp-game-lib-11-games](https://github.com/abagames/crisp-game-lib-11-games) | 8 | `6fea4eefe2deaa42706051d96a04ec9e36104696` | |
| | [crisp-game-lib-portable-sdl](https://github.com/joyrider3774/crisp-game-lib-portable-sdl) | 122 | `04bc2c4cfc2853628eda25a46190bb7ac6fe216a` | |
|
|
| These source collections carry MIT licenses, Copyright 2021 or 2022 ABA Games. |
| Their exact license texts and source links are retained in |
| [`LICENSE-Crisp`](runtime/LICENSE-Crisp). Keep the applicable copyright and permission |
| notices with redistributed copies or substantial portions of those sources. |
| These source licenses are distinct from the undeclared license of the converted |
| MinAtar Synth implementation. |
|
|
| ## Original MinAtar games |
|
|
| Credit **Kenny Young and Tian Tian**, authors of |
| [MinAtar](https://github.com/kenjyoung/MinAtar). The five held-out tasks are |
| `asterix`, `breakout`, `freeway`, `seaquest`, and `space_invaders`, using |
| **MinAtar 1.0.15**. Their original source is included in their task prompts with |
| its author headers. The package version, source-file paths, and source digests |
| are recorded rather than asserting an unverified Git release commit. |
|
|
| MinAtar is licensed under **GNU GPL version 3**. Its complete license text is |
| included as [`LICENSE-MinAtar`](runtime/LICENSE-MinAtar); preserve the source notices and |
| applicable GPL terms when distributing this material. This notice does not |
| relicense MinAtar under the cookbook's license. |
|
|
| ## Weight-space programming experiment |
|
|
| Credit **Rikiya Takehi, Jayden Teoh, Alex Zhang, and Omar Khattab** for the |
| [weight-space programming experiments](https://rikiyat.github.io/blog/weight-space-programming/), |
| especially [Experiment 5: writing policy weights from scratch](https://github.com/RikiyaT/lm_understand-logs/tree/4d09ba6d6a7e891dd8063974dfe81b3d78e3494f/exp5_write_from_scratch). |
| The benchmark follows that one-response, no-tools weight-programming setup. |
| The experiment's prompt/log materials are attributed under |
| [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), separately from the |
| GPL-licensed MinAtar source embedded in those prompts. The expanded game corpus |
| and model-training experiments are subsequent adaptations, not results from |
| the original experiment. |
|
|
