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README.md

@@ -33,3 +33,95 @@ configs:
   - split: train
     path: data/train-*
 ---
+# 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](https://github.com/hardmaru/slimevolleygym/blob/8ac22434fc5a587a3395311f7854aeda17d10303/slimevolleygym/slimevolley.py),
+[feedforward policy](https://github.com/hardmaru/slimevolleygym/blob/8ac22434fc5a587a3395311f7854aeda17d10303/slimevolleygym/mlp.py).
+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](./slime_bench.py) uses an isolated legacy
+Gym environment; it does not modify the training environment. From the outer-loop
+repository root:
+
+```bash
+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:
+
+```python
+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`.