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Expert RL Environments 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 environments.

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Advancing Agent Performance Through Repeatable Environments

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

Structured RL Environment Capabilities

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

UI Clones

Interactive replicas of enterprise and consumer apps such as Jira, Salesforce, and Zendesk. Workflows are defined with domain experts to ensure realism, and per-task verifiers confirm completion against golden states. These environments capture issue creation, sprint planning, and other critical flows for computer-use agents.

Backend Environments

MCP-based environments that expose APIs and tool calls for function-calling agents. Policies, database schema, and realistic seed data are created with SMEs, providing authentic records. They are used for evaluating and training agent behavior at scale.

Trajectory Generation

Controlled RL environment runs produce gold-standard trajectories for supervised fine-tuning. Tasks are structured for reuse across prompts and can be replayed for consistency. Each dataset supports curriculum progression from simple to complex scenarios.

Reward Model Training

Environments generate labeled trajectories that strengthen reward functions for RLHF. Verifier-driven reward and penalty signals provide the basis for reliable outcomes. This setup accelerates robust reward model development by ensuring every label has consistent QA.

Observability & Analytics

Harnesses replay scenarios and validate outcomes across prompts, models, and versions. Labs receive evaluation reports with pass/fail metrics and trajectory traces. This enables consistent A/B comparisons and tracking of agent performance over time.

Custom Environments

Bespoke environments support multi-tool workflows across role and function. Each is packaged for repeatable and structured testing, then delivered with SOPs, guardrails, and escalation paths aligned to policy. These environments replicate client-specific contexts while maintaining research-grade standards.

Scale and flexibility

Turing RL environments 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 Environments 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 repeatability.

Evaluation reports

with pass/fail results and repeatability scenario replays

Standards trusted by frontier AI labs

Accelerate agent performance with RL Environments

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 repeatability

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 Environments

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

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FAQs

What are Turing's RL Environments?

Turing's RL Environments are controlled, structured spaces, both UI and MCP, where AI agents can be trained, evaluated, and improved across real-world workflows. These environments include built-in prompts, verifiers, and seeded data to support structured post-training development.

What types of RL Environments does Turing offer?

Turing offers UI clone environments that replicate interactive software interfaces for agent workflows, and MCP-based backend environments for function-calling agents. These can be extended with trajectory generation setups, structured reward signals, and custom workflows tailored to specific tasks.

How do UI clone environments work?

UI clone environments are interactive replicas of enterprise and consumer applications. Agents perform actions through simulated mouse and keyboard input, and verifiers confirm completion by checking outputs against defined task states.

What are MCP environments used for?

MCP environments support agents that operate through tool calls or API-based actions. They include defined schemas, seeded data, and verifiers that validate tool-use behavior inside reproducible evaluation loops.

Can Turing build custom RL Environments for specific workflows?

Yes. Turing builds bespoke RL Environments that support multi-tool workflows tailored to specific roles or functions. Each environment is packaged with standard operating procedures, guardrails, and escalation paths aligned to the client’s evaluation needs.

How do Turing's RL Environments support agent improvement?

RL Environments provide reproducible traces, evaluator-reviewed trajectories, and pass or fail metrics that help benchmark agent behavior. These outputs can be used for supervised fine-tuning, reward-based improvement, and A/B comparison across model versions.

What makes Turing's RL Environments research-grade?

Turing’s environments are developed using R&D-driven standards with transparent and auditable pipelines. They are curated by domain experts, including PhD researchers, and incorporate human-in-the-loop feedback alongside AI-based review.

How many RL Environments does Turing provide?

Turing offers more than 1,000 RL Environments across enterprise and consumer applications, covering both UI and non-UI contexts with customizable multi-tool workflows for a wide range of roles and functions.

Ready to assess and expand the limits of your model's capabilities?

Start with an RL environment before you push to production, and confidently traverse your agent's prowess.

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