Domain-specific datasets for post-training evaluation and agent reasoning
Research-grade datasets and evaluation resources across finance, legal, medical, and economics domains.
Domain-specific datasets
Curated QA and reasoning tasks across specialized fields, built for depth, accuracy, and domain fidelity.
Applied Reasoning in Business, Law, and Finance
Complex QA tasks grounded in real-world decision-making, built for structured reasoning and scenario modeling.
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Clinical and Biomedical QA
Datasets sourced from medical literature and expert review, targeting diagnostic reasoning, treatment mapping, and biomedical understanding.
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Visual QA and Non-STEM Domains
Visual reasoning tasks and non-STEM QA sets for RLHF, CoT training, and interface QA across non-STEM domains.
Request Multimodal Non-STEM DatasetsBenchmarks and evaluation
Research-grade benchmarks and diagnostics built to surface failure modes and measure verified performance in domain-specific systems.
SWE-bench++
Evaluate coding agents on real GitHub tasks using containerized environments and verified trajectories.
Explore Benchmark
VLM-bench
Benchmark model reasoning on over 700 vision–language tasks grounded in STEM, logic, and world knowledge.
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Domain-Aware Reasoning Audit
Run targeted evaluations on domain-specific LLMs to test for accuracy, ambiguity handling, and compliance with structured knowledge.
Run a DiagnosticRL environments for domain-specific reasoning
Evaluate reasoning agents on real-world finance, economics, legal, and medical tasks, generate fine-tuning trajectories, and train reward models in reproducible, high-fidelity environments.
UI-Based RL Environments for Interface Agents
Run agents through domain-specific simulations, from economic modeling and policy forecasting to clinical decision support and legal analysis.
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MCP Environments for Function-Calling Agents
Train and evaluate agents using domain APIs, solvers, and datasets. Includes verifiers, structured evaluation pipelines, and adaptive reward mechanisms for high-fidelity performance tracking.
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Each RL environment includes prompts, verifiers, analytics harnesses, and trajectory outputs, enabling evaluation diagnostics, reward shaping, and supervised fine-tuning at scale.
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Accelerate domain-specific reasoning with Turing
From tax code to triage, our data helps you train and evaluate models with high-stakes reasoning in mind.