Rubric-based reasoning data pack for frontier model improvement
PhD-level reasoning data built for frontier AI teams working on RL, post-training, evaluation, reward modeling, and reasoning-failure analysis.
Get access to 1,100+ multi-domain scientific tasks across computer science, data science, and chemistry, paired with weighted atomic rubrics and golden answers.
Turning expert evaluation into machine-verifiable training signal
Turing builds evaluation-safe, expert-authored datasets for frontier model improvement. Our rubrics-based dataset extends that work from final-answer benchmarking into granular, criterion-level evaluation and reward signal.
- Per-criterion scoring for intermediate reasoning
- Rubrics aligned to prompt and golden answer
- Supports RL, reward modeling, and evaluation harnesses
- Authored by subject-matter experts
- Designed for advanced scientific and technical reasoning
- Built around self-contained, non-retrievable inputs
- Independent expert review
- Problem statement, rubric set, and golden answers checked for consistency, domain correctness, ambiguity, and rubric atomicity
- Tested across 16 evaluation rounds
- Pass rates between 0% and 50%
- Designed to remain discriminative for current state-of-the-art systems
Multi-domain coverage for advanced reasoning models
Custom expansions can be scoped for additional subdomains, difficulty levels, and specific model-improvement workflows.
Algorithms, systems, machine learning, programming languages, formal methods, databases, and data engineering
Business analytics, finance, healthcare, supply chain, HR, IT support, and research workflows
Organic, inorganic, organometallic, polymer, physical, and analytical chemistry
Built for RL, evaluation, and failure analysis
Each task measures more than final-answer correctness. Rubrics evaluate visible derivations, mechanism and structure identification, quantitative computation with explicit units, methodological choices, edge-case handling, and executable multi-step pipelines.
Each task is designed to support:
- Reinforcement learning with per-criterion reward signal
- Post-training and reward modeling
- Model comparison and regression testing
- Reasoning-trace quality analysis
- Scientific and engineering QA evaluation
- Failure-mode diagnosis across intermediate reasoning steps
