Multimodal datasets for post-training evaluation and agent reasoning
Get curated datasets and structured RL environments to stress-test models on the hardest multimodal tasks, including noisy speech, vision-language QA, and interface reasoning.
Multimodal datasets
Curated datasets across audio, vision, and interface-agent tasks designed to test reasoning and generalization across modalities.
Audio Datasets
Train models on nuanced, multilingual audio data across 60+ locales, covering disfluency, emotion, prosody, overlapping speech, and diarized multi-speaker tasks.
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Vision Datasets
Evaluate visual perception and reasoning through paired image-text tasks and STEM-based multimodal QA.
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Interface & Agent Datasets
Train and test interface-aware agents using GUI supervision, process traces, and interaction datasets grounded in real-world workflows.
Request Interface Agent DatasetsBenchmarks and evaluation
Research-grade benchmarks and diagnostics built to surface failure modes and measure verified performance in multimodal systems.
VLM-Bench
Benchmark model reasoning on over 700 vision–language tasks grounded in STEM, logic, and world knowledge.
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Evaluation Diagnostics
Run structured evaluations to identify model weaknesses and failure modes across multimodal domains.
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STEM VQA Sets
Stress-test models on graduate-level visual reasoning tasks in STEM domains.
Run a diagnosticRL environments for multimodal agents
Evaluate agents on real tasks, generate fine-tuning trajectories, and train reward models in reproducible, high-fidelity environments.
UI-Based RL Environments for Interface Agents
Evaluate computer-use agents inside interactive UI clones of apps like Jira, Salesforce, and Zendesk. These environments simulate real human interaction via mouse/keyboard input and event tracking.
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MCP Environments for Function-Calling Agents
Train agents on function calling and tool execution inside sandboxed server environments. Includes tool schemas, reward verifiers, and seed databases.
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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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Ready to stress-test your multimodal model?
Work with Turing to generate, evaluate, or scale multimodal data tailored to your use case.