---
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"
}
```