README.md

metadata

pretty_name: Procedural Pile
license: cc-by-4.0
task_categories:
  - text-generation
  - question-answering
language:
  - en
dataset_info:
  features:
    - name: task
      dtype: large_string
    - name: prompt
      dtype: large_string
    - name: answer
      dtype: large_string
    - name: metadata
      dtype: large_string
    - name: level
      dtype: int64
    - name: mode
      dtype: large_string
tags:
  - reasoning
  - procedural
  - synthetic
  - pretraining
  - formal
size_categories:
  - 10M<n<100M

Task gallery · Source · Paper · RLVR dataset

Procedural Pile is a synthetic corpus of verifiable reasoning problems generated by Reasoning Core. It is intended for continued pretraining, mid-training, and supervised fine-tuning.

Answers come from procedural generators and task-specific solvers or checkers, rather than language-model generation. The corpus spans mathematics, formal logic, planning, graphs, parsing, code, structured data, and other symbolic domains. Difficulty is controlled continuously within each task.

Reasoning Core is optimized for transferability: task selection and difficulty ranges are guided by reproducible measurements of transfer, solvability, and shortcut resistance.

---

Load

from datasets import load_dataset

dataset = load_dataset("reasoning-core/procedural-pile")

No configuration name is required. The dataset provides train and test splits.

Dataset structure

Field	Description
task	Reasoning task identifier
prompt	Model input
answer	Canonical target answer
metadata	JSON-encoded generation and validation metadata
level	Difficulty level
mode	instruct, few_shot, or verification

Most examples use direct instruction format. A smaller share adds one in-context demonstration or asks the model to verify a candidate answer.

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Task catalogue

The corpus currently contains 50 task families. The task gallery includes a worked example for each one.

Area	Tasks
Mathematics & formal methods · 10	arithmetics · math_word_problem · equation_system · combinatorics_formula_selection · planar_geometry_relations · lean_candidate_compilation · lean_missing_line · metamath_core_select · metamath_entailment · sequential_induction
Logic & inference · 10	logic_formalization · logic_nli · logic_qa · defeasible_nli · multistep_nli · multistep_abduction · multistep_evidence_retrieval · qualitative_reasoning · qualitative_causal_reasoning · belief_tracking
Symbolic transformations · 7	lambda_reduction · rewrite_system · unification_entailment · set_expression · set_missing_element · string_transduction · analogical_case_matching
Planning, state & graphs · 7	planning · constraint_satisfaction · grid_navigation · reference_tracking · coreference · graph_pathfinding · graph_successors
Language & formal languages · 5	parsing_derivation · regex_following · regex_reasoning · constrained_continuation · syntax_error_detection
Structured data & code · 7	table_qa · table_equivalence · table_statistics · code_analysis · code_execution · code_runnability · program_synthesis
Games & probability · 4	game_best_move · game_forced_win · most_probable_evidence · most_probable_outcome

Citation

@article{reasoningcore2026,
  title   = {Reasoning Core: A Scalable Procedural Data Generation Suite for Symbolic Pre-training and Post-Training},
  author  = {Lacombe, Valentin and Quesnel, Valentin and Sileo, Damien},
  journal = {arXiv preprint arXiv:2603.02208},
  year    = {2026},
  url     = {https://arxiv.org/abs/2603.02208}
}