Improve Real-World Model Reasoning
Improve frontier model performance with proprietary data, structured RL environments, coding benchmarks, and multimodal tests built for real-world reasoning.
Core Capabilities
Three capability areas that support structured post-training: curated datasets, RL environments, and benchmarks.
Data Packs
Curated datasets for RL, coding, audio, STEM, and multimodality. Built for evaluation clarity, reliability, and reward shaping.
Request Sample Data
RL Environments
Structured and state-controlled worlds for agent training and evaluation. Supports browser-use, function calling, and enterprise simulations.
Request RL Environments
Benchmarks
Research-grade reasoning progress tests grounded in real-world scenarios. Includes SWE-bench++, VLM-bench, and other private evaluation suites.
Request Benchmark Access
Structured Workflows for Post-Training
Every post-training project follows a repeatable workflow that helps you evaluate models, generate new data, and improve reasoning performance.
Evaluate Your Model
Identify reasoning gaps using structured tests, RL environments, and coding benchmarks.
Run Benchmarks
Generate Frontier Data
Create evaluator-verified datasets that accelerate training, reward modeling, and error recovery.
Build Data
Optimize Your Model Performance
Fine-tune agents with calibrated RL loops, synthetic data, and domain-specific reasoning tasks.
Deploy Improvements
Why Labs Choose Turing
We turn every model deployment into repeatable success with:
On-demand frontier talent
Access vetted engineers, researchers, PhDs, and Olympiad-level experts trained in ambiguity detection, rubric QA, and reasoning evaluation.
ALAN human-AI platform
Structured loops that combine evaluators, AI reviewers, and synthetic data for consistent rubric-aligned QA.
In-house research and delivery
Research team builds benchmarks, frameworks, and evaluation systems used across model improvement cycles.
A repeatable post-training system
Every workflow follows our Five-Step Framework so results stay measurable and comparable.
Migration and vendor replacement
Transition from legacy vendors with stable QA, evaluator continuity, and reliable task logic.
From Research to Results
Explore technical contributions and case studies from leading lab partnerships, designed to push reasoning, reward learning, and post-training QA forward.
Ready to Train Smarter Models?
Request data packs, RL environments, or benchmark diagnostics, all designed for post-training maturity.