Turing at NeurIPS 2025
Explore RL environments, benchmarks, and expert-verified datasets built for post-training, reinforcement learning, and structured model evaluation across STEM, multimodality, tool use, and coding.
Trusted by AI leaders and enterprises
RL environments
UI and MCP environments for agent training and evaluation. Each environment includes prompts, verifiers, and reward logic for controlled experimentation and validated results.
Transactional environments
Test agents in realistic ordering, cart, and fulfillment workflows with embedded verifiers and step logic.
Support-resolution environments
Evaluate multi-step reasoning for ticket triage, routing, and knowledge retrieval in helpdesk-style tasks.
Project-management environments
Run MCP-based agent evaluations with live schema validation and verifier-scored task logic.
Coding and benchmarking
Deterministic systems for measuring model reasoning, synthesis, and code understanding on verifiable tasks.
SWE-bench++
Software reasoning benchmark using real GitHub issues and validated fixes.
VLM-bench
Multimodal reasoning benchmark across STEM and business tasks using vision-language inputs.
CodeBench
Deterministic evaluation for code models with structured prompts and ideal responses.
Data
Expert-verified datasets for post-training and evaluation, built from auditable pipelines with human-in-the-loop QA.
Catalog highlights
- Coding: Real-world repo tasks and reasoning traces.
- STEM: Research-grade STEM and bioinformatics tasks with executable reasoning and code.
- Multimodality: Audio, image, and GUI reasoning datasets.
- Domain-specific: Finance, medical, legal, and economics.
- Robotics & Embodied AI: Imitation learning and embodied reasoning.
- Custom: Scoped experiments, edge cases, or novel-modality datasets.
NVIDIA Data Filtering Challenge award
Evening discussion with NVIDIA and Turing leadership on model maturity and frontier evaluation, followed by the NVIDIA Challenge awards.
