Datasets:

TuringEnterprises
/

Rubric-Graded-Reasoning

license: mit
task_categories:
  - question-answering
  - text-generation
language:
  - en
tags:
  - rl
  - rubric-evaluation
  - rubrics-graded
  - llm-eval
  - computer-science
  - data-science
  - chemistry
  - scientific-reasoning
size_categories:
  - n<1K
pretty_name: Rubrics-Graded Reasoning — Computer Science, Data Science, Chemistry
configs:
  - config_name: Rubric-CS
    data_files: Rubric-CS/data.jsonl
  - config_name: Rubric-DS
    data_files: Rubric-DS/data.jsonl
  - config_name: Rubric-Chem
    data_files: Rubric-Chem/data.jsonl

Rubrics-Graded Reasoning — Computer Science, Data Science, Chemistry

A multi-domain reasoning dataset built to improve frontier models by revealing their failures and turning expert grading into training signal.

The dataset pairs self-contained tasks with weighted rubrics across three domains — Computer Science, Data Science, and Chemistry — turning expert evaluation into training signals that boost frontier-model reasoning.

Explore the full Rubric-based reasoning data pack: https://go.turing.com/advanced-reasoning-rubrics

Goal

This release exists to showcase Turing's rubrics-based advanced PhD-level reasoning datasets capability. Every task is the product of doctoral-level expert authorship and review, paired with atomic process-level rubrics that turn correctness into machine-verifiable signal. The 150 tasks here (50 per domain) are a public sample of the methodology and quality bar Turing applies across thousands of RL tasks delivered to frontier-model partners.

Why This Dataset

Standard benchmarks are useful, but they are limited for model improvement when scoring is mostly final-answer based, reasoning visibility is partial, or process-level rubrics are missing. This dataset addresses those gaps with three strengths:

Productive difficulty: 0%–50% pass rates across 16 evaluation rounds against recent frontier models.
Visible reasoning: open-ended tasks stress derivations, mechanisms, calculations, units, and structure/product identification.
Training signal: atomic weighted rubrics score both final answers and intermediate reasoning steps.

This dataset is high-quality with unique prompts, weighted rubrics, expert-authored content, and broad domain coverage.

Configurations

The repository contains three configurations:

Config	Tasks	Schema
`Rubric-CS`	50	`domain`, `prompt`, `rubrics`, `golden_answer`
`Rubric-DS`	50	`domain`, `prompt`, `rubrics`, `golden_answer`, `datasets`
`Rubric-Chem`	50	`domain`, `prompt`, `rubrics`, `golden_answer`

Load any configuration with:

from datasets import load_dataset

cs = load_dataset("TuringEnterprises/Rubric-Graded-Reasoning", "Rubric-CS")
ds = load_dataset("TuringEnterprises/Rubric-Graded-Reasoning", "Rubric-DS")
chem = load_dataset("TuringEnterprises/Rubric-Graded-Reasoning", "Rubric-Chem")

print(cs["train"][0]["prompt"])
print(cs["train"][0]["rubrics"])

Each rubrics entry is an object with two fields:

criterion — one atomic verification criterion
weight — relative score weight for that criterion

Rubric-CS — Computer Science

A sample of off-the-shelf rubric-based reasoning data covering algorithms, systems, databases, machine learning, and programming languages / compilers.

Every task was authored and reviewed by subject-matter experts, paired with atomic weighted rubrics that target precise correctness and intermediate reasoning, and accompanied by a golden_answer that derives every quantity from first principles.

CS Subdomain Coverage

Subdomain	Tasks
Algorithms & Data Structures	33
Computer Systems & Operating Systems	8
Machine Learning & Artificial Intelligence	4
Programming Languages, Compilers & Formal Methods	3
Database Systems & Data Engineering	2

Tasks span classical material (recovery protocols, scheduling, cache coherence, complexity bounds) and modern engineering scenarios (distributed storage, kernel simulation, model architectures, compiler IR transformations).

Rubric-DS — Data Science

A sample of off-the-shelf rubric-based reasoning data covering real-world analytical scenarios across business operations, finance, healthcare, supply chain, HR analytics, scientific research, and IT operations.

Most tasks are grounded in a real dataset (referenced by the datasets field) — a small number of tasks (e.g., simulation or estimator-comparison problems) have no companion files and operate entirely on prompt-provided parameters. The model is expected to read the data (when provided), design and execute a multi-step analysis, and produce a structured output. The golden_answer is a runnable, deterministic implementation of the full pipeline.

DS Subdomain Coverage

Subdomain	Tasks
Business Operations & Analytics	23
Financial & Accounting	10
Science, Environment & Research	6
Healthcare	4
Inventory & Supply Chain	3
Human Resources & Employee Management	3
IT & Support	1

Data Files

Each DS task references one or more data files in its datasets field. The companion files live in Rubric-DS/files/<descriptive_name>/ inside the repository, with one subfolder per task named after the task's analytical context:

Rubric-DS/files/
├── financials_inverse_valuation_signal/
│   └── Financials.csv
├── orders_data_quality_risk_ranking/
│   ├── Orders.csv
│   └── Details.csv
├── hospital_staffing_optimization/
│   ├── doctors.csv
│   ├── locums.csv
│   ├── patients.csv
│   └── ...
└── ...

Resolve a relative path against the repo root:

import os
from huggingface_hub import snapshot_download
from datasets import load_dataset

# Get the repo files (data.jsonl + the actual CSVs)
repo_root = snapshot_download(repo_id="TuringEnterprises/Rubric-Graded-Reasoning",
                              repo_type="dataset")

ds = load_dataset("TuringEnterprises/Rubric-Graded-Reasoning", "Rubric-DS")
task = ds["train"][0]

for file_ref in task["datasets"]:
    full_path = os.path.join(repo_root, file_ref)
    # ... read the file at full_path with pandas / csv / etc.

Rubric-Chem — Chemistry

A chemistry reasoning subset pairing self-contained tasks with weighted rubrics, turning expert evaluation into training signals that boost frontier-model reasoning. This subset is the 50-task curated demo of a larger 500-task chemistry rubric corpus.

Comparison to Other Chemistry Benchmarks

Benchmark	Reasoning Visibility	Process-Level Rubrics	RL Training Value
MMLU Chemistry	Low: mostly final-answer selection	No	Low for frontier-model improvement
GPQA Chemistry	Medium: expert-level questions, limited reasoning trace	No	Useful for hard eval, limited for process supervision
ChemBench	Medium: broad chemistry coverage	Varies by task	Useful for eval, less compact for RL reward data
Rubric-Chem	High: derivations, mechanisms, calculations, and units	Yes: atomic weighted rubrics	High: final-answer and process-credit reward signals

Chemistry Subset Coverage

The 50-task demo subset is deliberately broad, sampling across the full breadth of chemistry rather than concentrating on any single subfield. Tasks span 37 distinct subdomains spanning organic, inorganic, organometallic, polymer, physical, and analytical chemistry:

Analytical chemistry, Biochemical redox chemistry, Boron cluster chemistry, Chemical kinetics, Combustion spectroscopy, Computational chemistry, Coordination chemistry, Electron-transfer kinetics, Heterocyclic synthesis, Inorganic main-group chemistry, Kinetic theory, Liquid-phase equilibrium, Main-group chemistry, Medicinal organic chemistry, Nuclear / reactor chemistry, Organic / heterocyclic chemistry, Organic redox chemistry, Organic synthesis, Organic synthesis / HRMS, Organogallium chemistry, Organometallic chemistry, Organometallic synthesis, Photopolymerization, Physical chemistry, Polymer chemistry, Polymer kinetics, Polymer networks, Polymer physical chemistry, Polymer statistics, Quantum chemistry, Reaction dynamics, Solution kinetics, Statistical thermodynamics, Supramolecular coordination chemistry, Thermal analysis / materials, Thermodynamics, Thermodynamics / heat recovery.

The full 500-task dataset extends this rubric-based coverage across all subfields. This demo includes quantitative tasks (kinetics, thermodynamics, spectroscopy, polymer calculations) and structural/mechanistic tasks (reaction pathways, product identification, organometallic structure, electrochemical synthesis, pharmacophore analysis).

Intended Uses

RL reward modeling and post-training
Process-level evaluation across CS, DS, and Chemistry
Model comparison and regression testing
Reasoning failure analysis for scientific and engineering QA systems

Authors

Juhi Parekh, Nicolas Morant, Jiyuan Liu, Gabriel Araujo, Govind K Rajesh, Ben Steinberg, Anubhav Elhence and the Pearl team at Turing Enterprise.

Citation

@dataset{turing_2026_rubric_graded_reasoning,
  title        = {Rubric-Graded-Reasoning: A Curated Multi-Domain Reasoning Dataset for Advancing Frontier LLMs},
  author       = {
    Parekh, Juhi and
    Morant, Nicolas and
    Liu, Jiyuan and
    Araujo, Gabriel and
    K Rajesh, Govind and
    Steinberg, Ben and
    Elhence, Anubhav and
    Pearl Team at Turing Enterprise
  },
  year         = {2026},
  publisher    = {Hugging Face}
}

Contact

For inquiries, please open a discussion on the dataset repository.

License

MIT