SlimeBench

SlimeBench measures offline imitation of a deterministic SlimeVolley controller. It contains 64 rows, each with 2,048 training and 100 test conversations. Each conversation comes from a different independently seeded, complete rollout: 137,472 rollouts total, with disjoint seeds across every row and split.

A conversation contains only a user message with a bracketed list of 12 numbers and an assistant message with one letter from A through F. There are no feature names, action descriptions, or system messages. Each row has its own random permutation of observation coordinates and assignment of the six effective actions to letters. Both mappings are fixed within the row and distinct across all rows. The public columns are row_id, num_train, num_test, train_data, test_data; the latter two contain lists of two-message conversations.

Teacher and sampling

The teacher is the upstream feedforward CMA-ES policy, using the original zoo/cmaes/slimevolley.cma.64.96.best.json weights. It plays against the built-in recurrent BaselinePolicy. Both simulator and opponent state are reset for each rollout; upstream env.reset() alone does not reset the opponent's recurrent state. The pinned upstream revision is 8ac22434fc5a587a3395311f7854aeda17d10303: environment, feedforward policy. SlimeVolleyGym is distributed under Apache-2.0.

The policy receives float32 observations, serialized with nine significant digits so every input round-trips exactly. Positive network outputs press the three buttons; simultaneous forward/backward presses cancel, leaving six effective actions. These are mapped to letters without changing the rollout dynamics. Every published label is recomputed from its serialized input before upload.

Reservoir sampling selects one timestep uniformly within each complete rollout, including games that reach the 3,000-step limit. No action balancing, win filtering, or loss filtering is performed. Thus the dataset preserves the teacher's natural action frequencies under this episode-uniform sampling scheme; it does not weight longer episodes more heavily. Sampled conversations are shuffled within each split.

Accuracy measures agreement with this teacher, not optimality or closed-loop playing strength. Natural action frequencies can be highly imbalanced: report majority-class baselines when interpreting accuracy. Hidden random action mappings give expected zero-example accuracy of 1/6 over mappings, assuming the evaluator receives no mapping information. They do not make every finite row balanced.

Reproduce

The generator uses an isolated legacy Gym environment; it does not modify the training environment. From the outer-loop repository root:

uv venv --python 3.11 /tmp/slimebench-venv
uv pip install --python /tmp/slimebench-venv/bin/python \
  numpy==1.26.4 gym==0.23.1 opencv-python-headless==4.10.0.84 \
  datasets==4.4.1 pyarrow==21.0.0 huggingface-hub==0.36.0 python-dotenv==1.2.4
git clone https://github.com/hardmaru/slimevolleygym.git /tmp/slimebench-upstream
git -C /tmp/slimebench-upstream checkout 8ac22434fc5a587a3395311f7854aeda17d10303
OPENBLAS_NUM_THREADS=1 /tmp/slimebench-venv/bin/python \
  data_preparation/benchmarks/slime_bench.py \
  --source-dir /tmp/slimebench-upstream \
  --output-dir local_data/slime_bench --workers 32 --upload

The default upload destination is aklein4/SlimeBench, configuration default, split train. The 64 outer rows are uploaded together; the support/test distinction is within each row. Authentication uses the existing Hugging Face login or HF_TOKEN. Use --rows 2 --num-train 16 --num-test 8 --workers 2 and a different output directory without --upload for a smoke test.

Completed rows are checkpointed and reused on reruns with the same source and generation settings. Worker count can change on resume. Local checkpoints contain the private permutations, original observations, rollout seeds, sampled timesteps, and episode returns; these must not be exposed as model inputs and are not uploaded. The output directory also contains slime_bench.jsonl, manifest.json, validation.json, and (after publication) upload.json.

Load conversations with:

from datasets import load_dataset
rows = load_dataset("aklein4/SlimeBench", split="train")
support = rows[0]["train_data"]
test = rows[0]["test_data"]

Published run

• 137,472 conversations from distinct rollouts.
• Mean teacher rollout return: 1.1126.
• Exact train/test prompt overlaps within rows: 0.
• Generation seed: 42.
• JSONL SHA-256: b04fabf11357dc0c63b307e6799a0d4d58b35c08539c06718cbd4aa675b35242.