STEM datasets for post-training evaluation and reasoning
Human-authored datasets, benchmarks, and tools for evaluating and improving scientific and mathematical reasoning in LLMs.
STEM datasets
Human-authored datasets across STEM domains to support scientific accuracy, alignment training, and symbolic rigor at scale.
Math, Physics, Chemistry, and Biology Datasets
Chain-of-Thought + Stepwise Reasoning Packs
High-Throughput Training Data
Lean-Based Proof QA Datasets
Benchmarks and evaluation
Rubric-aligned benchmarks and structured diagnostics that surface STEM-specific model weaknesses and reasoning gaps.
VLM-Bench
GPQA, AIME, and MMLU-Pro Comparisons
High-Difficulty STEM Benchmarks
RL environments for STEM workflows
Evaluate agents on real-world STEM tasks, generate fine-tuning trajectories, and train reward models in reproducible, high-fidelity environments.
UI-Based RL Environments for Interface Agents
MCP Environments for Function-Calling Agents
End-to-End Evaluation and Training Loops
Research and case studies
FAQs
What STEM domains does Turing provide datasets for?
Turing offers human-authored datasets across math, physics, chemistry, and biology designed to test logical structure, problem-solving accuracy, and formal rigor grounded in real-world scientific domains.
What are Chain-of-Thought datasets used for?
Chain-of-Thought datasets are trace-based reasoning examples scored for fidelity, designed specifically for training and reward shaping to improve stepwise reasoning capabilities.
Does Turing provide benchmarks for evaluating LLM performance?
Yes, Turing offers rubric-aligned benchmarks including VLM-Bench with over 700 vision-language tasks, high-difficulty STEM benchmarks, and comparisons against known benchmarks like GPQA, AIME, and MMLU-Pro.
What are RL Eenvironments for STEM workflows?
Turing provides reproducible, high-fidelity environments where you can evaluate agents on real-world STEM tasks, generate fine-tuning trajectories, and train reward models, including UI-based environments and MCP environments for function-calling agents.
What is included in Turing's RL environments?
Each RL environment includes prompts, verifiers, analytics harnesses, and trajectory outputs, enabling evaluation diagnostics, reward shaping, and supervised fine-tuning at scale.
Does Turing offer Lean-based proof datasets?
Yes, Turing provides Lean-based proof QA datasets featuring iterative proof generation in Lean 4 paired with informal math questions, supporting symbolic reasoning and fine-tuned verification.
What is VLM-Bench?
VLM-Bench is Turing’s benchmark for vision-language reasoning, covering more than 700 tasks across STEM, logical inference, spatial reasoning, and real-world multimodal problem-solving.
Can Turing's datasets be used for both training and evaluation?
Yes. Turing’s data packs and datasets support post-training workflows including supervised fine-tuning, evaluator calibration, symbolic reasoning tasks, and structured evaluation across STEM domains.
Scale STEM reasoning with expert-built datasets
Train, fine-tune, or evaluate models on structured STEM tasks, backed by domain-reviewed data and traceable QA.
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