README.md

---
license: mit
task_categories:
- reinforcement-learning
tags:
- eleusis
- verifiers
- tool-use
- rule-induction
dataset_info:
  features:
  - name: rule_id
    dtype: string
  - name: label
    dtype: string
  - name: family
    dtype: string
  - name: code
    dtype: string
  - name: split
    dtype: string
  - name: uses_mainline
    dtype: bool
  - name: accepted_cards
    list: string
  - name: rejected_cards
    list: string
  - name: empty_mainline_accepted_cards
    list: string
  - name: empty_mainline_rejected_cards
    list: string
  - name: representative_acceptance_rate
    dtype: float64
  - name: rule_index
    dtype: int64
  - name: split_index
    dtype: int64
  - name: dataset_version
    dtype: string
  - name: source_split
    dtype: string
  - name: source_dataset_version
    dtype: string
  - name: oracle_max_turns
    list: int64
  - name: oracle_optimal_turns
    list:
      list: float64
  - name: oracle_expected_optimal_turns
    list:
      list: float64
  - name: oracle_methods
    list:
      list: string
  splits:
  - name: train
    num_bytes: 81960
    num_examples: 66
  - name: eval
    num_bytes: 32356
    num_examples: 26
  download_size: 116311
  dataset_size: 114316
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: eval
    path: data/eval-*
---

# Eleusis HF Rules

This dataset contains the HF-like Eleusis rule curriculum previously embedded in
`nph4rd/eleusis` as `v8-relational-curriculum-90`, republished as a standalone
rule dataset so the environment can switch rule sets by dataset repo.

Dataset version: `v1-hf-like-train-hf-eval-64`

## Splits

- `train`: 38 HF-like curriculum rules.
- `eval`: 26 HF benchmark-style rules, copied from the old internal `hf` split.

The dataset intentionally exposes only `train` and `eval`. A training run that
uses this dataset and evaluates on `split=eval` is therefore evaluating directly
on the HF benchmark-style rules.

## Family Distribution

Train:
- `pair_position`: 2
- `previous_card`: 23
- `static_combo`: 3
- `static_rank`: 6
- `static_suit_color`: 4

Eval:
- `pair_position`: 2
- `previous_card`: 11
- `static_combo`: 3
- `static_rank`: 6
- `static_suit_color`: 4


## Schema

Each row contains:

- `rule_id`: stable identifier.
- `label`: human-readable description for inspection only.
- `family`: rule family.
- `code`: Python predicate body or expression used by the environment verifier.
- `split`: published split (`train` or `eval`).
- `source_split`: original split from the embedded curriculum.
- `dataset_version`: this dataset version.
- `accepted_cards` / `rejected_cards`: empty-mainline card partitions.
- `representative_acceptance_rate`: acceptance rate over representative mainlines.

The environment should use `code` for scoring and should not expose `label` or
`code` to the model.