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.

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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.

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Benchmarks 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.

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RL 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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End-to-End Evaluation and Training Loops

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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Research and case studies

Case Study

Revealing Systemic Chart Reasoning Gaps with 20K+ Expert CoTs

Built a 20K-sample dataset to surface model failures in scientific chart reasoning, enabling more accurate eval, reward shaping, and subfigure calibration.

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Audio SFT- Enhancing AI with Real-World Spoken Prompt Training_Hero_1232-770

Resource

Audio SFT: Teaching AI to Understand Human Voice in Noisy, Real-World Scenarios

Audio SFT is a human-in-the-loop supervised training process that feeds LLMs with contextual, emotion-rich, and acoustically diverse audio prompts.

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Advanced, high-quality data for multimodal LLMs.png

Resource

Advanced Strategies for High-Quality, Scalable, and Diverse Data for Multimodal LLMs

This brief shares practical insights from Turing AGI Advancement’s recent collaboration with a leading AI lab to build large-scale multimodal datasets...

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Case Study

Building 7K+ High-Complexity SlideVQA Tasks Across 20+ Knowledge Domains

Created expert-verified multimodal QA prompts from real-world slide decks, targeting reasoning failures in large multimodal models (LMMs) across business, STEM, finance, and general knowledge.

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What types of multimodal datasets does Turing offer?

Turing provides curated datasets across audio, vision, and interface-agent tasks designed to test reasoning, perception, tool use, and cross-modal generalization in real-world workflows.

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.

What are Turing's audio datasets designed to test?

Turing's audio datasets evaluate multilingual perception and reasoning across tasks involving disfluencies, emotional cues, prosody, overlapping speech, and multi-speaker dialogue.

What are RL environments for multimodal agents?

Turing's RL environments for multimodal agents are reproducible settings where agents can interpret images, audio, or UI state, perform actions, and generate trajectories for evaluation or fine-tuning. These include UI clones and backend environments for tool-using agents.

What types of interface agent environments does Turing provide?

Turing provides UI-based RL Environments that replicate common software interfaces. These interactive environments support mouse and keyboard input, event tracking, and multi-step workflows for evaluating interface agents.

What do Turing's vision datasets evaluate?

Vision datasets from Turing evaluate visual perception and reasoning through paired image-text tasks and STEM-based multimodal QA.

What is included in each RL Eenvironment?