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Expert RL Gyms Built for Frontier Standards

Controlled reinforcement learning environments for training and evaluating agents. Start with scoped experiments to validate fit before scaling across custom or pre-built RL gyms.

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Advancing Agent Performance Through Reproducible Environments

Turing’s RL Gyms provide structured, reproducible UI and non-UI environments where agents can be evaluated, trained, and iterated against real-world workflows. Each gym includes prompts, verifiers, and seed data—packaged for controlled experimentation and reproducible research.

Structured RL Gym Capabilities

Each capability is available as a scoped environment. Experiments are designed to validate scope and performance before larger-scale integration.

UI Clones

Fully interactive replicas of enterprise apps (e.g. Jira, Salesforce, Zendesk), with SME-defined workflows and per-task verifiers to test computer-use agents.

Backend Gyms

MCP-based environments with policies, database schema, and realistic seed data created with domain experts, used for evaluating and training function-calling agents.

Trajectory Generation

Controlled RL Gym runs that generate gold-standard, reusable agent trajectories for supervised fine-tuning across tasks and difficulty levels.

Reward Model Training

Environments configured for RLHF with verifier-driven signals to produce labeled trajectories and accelerate robust reward model development.

Observability & Analytics

Harnesses that replay scenarios, validate outcomes, and track agent performance across versions with consistent A/B testing metrics.

Custom Gyms

Client-specific multi-tool workflows packaged for reproducibility and A/B testing, delivered with SOPs, guardrails, and escalation paths aligned to policy.

Scale and flexibility

Turing RL Gyms are designed to match the scope of both enterprise and research demands.

1000+

environments across enterprise and consumer applications, both UI and non-UI.

Custom

multi-tool workflows supporting any role–function combination in enterprise contexts.

Designed for continuous improvement

Turing RL Gyms are full loops from evaluation to iteration, not static testbeds.

Observability and analytics

to track performance across agent versions.

Closed-loop data

for supervised fine-tuning and reinforcement learning.

Expert prompts and verifiers

created by domain specialists for reproducibility.

Evaluation reports

with pass/fail results and reproducible scenario replays

Standards trusted by frontier AI labs

Accelerate agent performance with RL Gyms

R&D-driven standards

Criteria and taxonomies aligned with research use

Transparent, auditable pipelines

Trace every trajectory and evaluation run end-to-end

Elite, domain-specific talent

PhDs, Olympiad-level specialists, and vetted SMEs

Human-in-the-loop + AI feedback loops

Combined review to catch edge cases and ensure reproducibility

Domain-expert collaboration

Policies, database schema, and realistic seed data records built with SMEs

Application-level specificity

Workflows designed for real tools (e.g., Jira: issue creation, sprint planning, backlog grooming)

Accelerate agent performance with RL Gyms

Get your own RL Gym and run agents in reproducible, high-fidelity environments tailored to your workflows.

Request RL Gym