| --- |
| dataset_info: |
| features: |
| - name: row_id |
| dtype: int64 |
| - name: num_train |
| dtype: int64 |
| - name: num_test |
| dtype: int64 |
| - name: train_data |
| list: |
| list: |
| - name: role |
| dtype: string |
| - name: content |
| dtype: string |
| - name: test_data |
| list: |
| list: |
| - name: role |
| dtype: string |
| - name: content |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 22791867 |
| num_examples: 64 |
| download_size: 9156863 |
| dataset_size: 22791867 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| --- |
| # SlimeBench-balanced |
|
|
| Action-balanced behavioral cloning of the deterministic SlimeVolley CMA-ES teacher. |
| There are 64 rows, each with 2048 training and 100 test conversations. Each |
| conversation comes from its own independently seeded complete rollout. Seeds are |
| globally disjoint across rows and splits and distinct from the original |
| SlimeBench production run. No examples are duplicated to fill action quotas. |
|
|
| Each row and split has nearly equal counts of the six underlying actions: |
| 341 or 342 training examples per action, and 16 or 17 test examples per action. |
| Remainder recipients and example order are randomized independently per split. |
| For each assigned action, an independent teacher rollout is completed and |
| reservoir sampling selects uniformly among timesteps where the teacher chooses |
| that action. If the action never occurs, the rollout is discarded and a fresh |
| seed is tried. Rollouts are not selected by reward, wins, or episode length. |
|
|
| This samples eligible rollouts equally, then matching timesteps uniformly; |
| it does not pool all matching timesteps across games. It intentionally changes |
| the natural state/action distribution. Evaluation measures action-balanced |
| agreement with this teacher, not closed-loop reward or optimality. |
|
|
| The six effective actions are no movement, forward, forward+jump, jump, |
| backward+jump, and backward. For each row, their A–F labels and the order of the |
| 12 observation coordinates are independently randomly permuted. Both mappings |
| are fixed within the row and unique across rows. Conversations contain only an |
| anonymous numeric list and a single-letter answer, with no system instruction. |
| Every published action is checked against the teacher on the serialized float32 |
| input. Original feature names, private permutations, seeds, and rollout metadata |
| are kept locally and excluded from the published dataset. |
|
|
| Public columns: `row_id`, `num_train`, `num_test`, `train_data`, `test_data`. |
| The Hub split `train` contains the 64 outer rows; each row contains its own |
| training and held-out lists of two-message conversations. |
|
|
| ## Reproduce |
|
|
| Use Python 3.11, NumPy 1.26.4, Gym 0.23.1, datasets 4.4.1, pyarrow 21.0.0, |
| huggingface-hub 0.36.0, python-dotenv, and opencv-python-headless 4.10.0.84. |
| Clone [SlimeVolleyGym](https://github.com/hardmaru/slimevolleygym) at revision |
| `8ac22434fc5a587a3395311f7854aeda17d10303`. The original feedforward weights are |
| `zoo/cmaes/slimevolley.cma.64.96.best.json`, SHA-256 |
| `3523cca6459ea6a979b98b19de8466eba8d5d1318ce6fa71f8f7cd084f38ce9b`. |
| The opponent is the built-in recurrent BaselinePolicy, reset every rollout. |
| Positive teacher outputs press buttons; simultaneous opposite directions cancel. |
| SlimeVolleyGym is distributed under Apache-2.0. |
|
|
| Both `slime_bench_balanced.py` and its helper `slime_bench.py` are provided with |
| the dataset. In the outer-loop repository: |
|
|
| ```bash |
| OPENBLAS_NUM_THREADS=1 /tmp/slimebench-venv/bin/python \ |
| data_preparation/benchmarks/slime_bench_balanced.py \ |
| --source-dir /tmp/slimebench-upstream \ |
| --output-dir local_data/slime_bench_balanced --workers 32 --upload \ |
| --output-dataset aklein4/SlimeBench-balanced |
| ``` |
|
|
| The default seed is 10000000. Completed rows are checkpointed for resumption; |
| the manifest guards against changed source/settings. Publication requires the |
| full production size. The validation report includes split action counts, |
| unique rollout count, rejected rollout count, and dataset checksum. |
|
|
| ```python |
| from datasets import load_dataset |
| rows = load_dataset("aklein4/SlimeBench-balanced", split="train") |
| ``` |
|
|
| ## Validation |
|
|
| ```json |
| { |
| "rows": 64, |
| "conversations": 137472, |
| "distinct_rollouts": 137472, |
| "attempted_rollouts": 137472, |
| "rejected_rollouts": 0, |
| "cross_split_duplicate_prompts": 0, |
| "mean_rollout_return": 1.1095204841713222, |
| "action_counts": { |
| "train": [ |
| 21850, |
| 21839, |
| 21843, |
| 21839, |
| 21852, |
| 21849 |
| ], |
| "test": [ |
| 1066, |
| 1064, |
| 1071, |
| 1068, |
| 1072, |
| 1059 |
| ] |
| }, |
| "jsonl_sha256": "39a7b12517a587e6ed00d291a7a84e362f506cbfe29dadfdb6d2330bb7eb035e" |
| } |
| ``` |
|
|
