Best RL Environment Companies in 2026
Patronus AI TeamOctober 202610 min read
The main RL environment companies in 2026 are Patronus AI, Scale AI, Surge AI, Mercor, Turing, Prime Intellect, HUD, Fleet and Mechanize. They sell different things. Some build finished environments, some supply expert human data, and some give teams a platform or open hub to build their own. To choose, match the company to the domain your model needs to learn, ask how its graders are checked for reward hacking, and confirm the security your data requires.
Agents now learn long, multi-step tasks in RL environments, each a sandbox with the tools an agent needs, a problem to solve and a reward that scores the result. An environment is only as useful as its problems and its grader. A task that is too easy teaches nothing, and a grader with a loophole teaches the model to exploit it, so choosing a builder is a research question as much as a purchase. This guide examines nine companies serving RL environment buyers in 2026, starting with Patronus AI, a behavioral research lab whose open benchmark FinanceBench has 340+ citations and 79,000+ Hugging Face downloads as of October 2026.
The nine entries sit at different layers, and it helps to read them that way. Patronus AI is a research lab that designs and builds environments and the data inside them. Scale AI, Surge AI and Turing are expert data companies that run large human data programs and now build environments too. Mercor is an expert network built on working professionals. Prime Intellect runs an open hub and training stack, and HUD is a platform for building environments in-house. Fleet and Mechanize build environments for particular kinds of work, simulated workplaces and software engineering.
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Key takeaways
- Research depth is a useful selection filter. A builder that publishes on how models fail is better placed to design tasks that teach and graders that hold. Patronus AI publishes at NeurIPS, ICML and ACL, including TRACE on reward hacking in code RL at ICML 2026.
- People who do the work should write and check the problems. Hard domains need domain experts. Patronus AI has experts review every environment, and expert data companies such as Surge AI and Mercor run large networks of professionals.
- A builder that trains on its own environments finds problems first. Training a model on a task shows whether it teaches anything and whether the grader can be gamed. Patronus AI runs its own post-training stack to measure what its tasks teach.
- Open hubs shorten setup time. Prime Intellect lists more than 2,500 community environments on its Environments Hub, built on the open-source Verifiers library.
- Shared interfaces reduce lock-in. OpenEnv, an open project published on Hugging Face, defines a common environment interface, and its technical committee includes Prime Intellect, Mercor and Fleet AI.
- Security requirements narrow the field. Regulated data needs audited controls. Patronus AI lists SOC 2, HIPAA and TISAX, and Scale AI lists FedRAMP High for government work.
Comparison table
All nine companies at a glance, in the order of this guide. Security shows what each company lists on its own site. Sort by any column marked with arrows.
| # | Company | Best for | Type | What it sells | How it checks quality | Security |
|---|---|---|---|---|---|---|
| 1 | Patronus AI | Hard domains, with environments built by a research lab | Research lab | Ready-made and custom RL environments, and parts such as simulated apps, mock websites and data | Expert review and automated checks on every environment, plus its own training runs to measure what tasks teach | SOC 2, HIPAA, TISAX |
| 2 | Scale AI | Data, evaluations and environments from one vendor | Data and evaluation | RL environments on web apps, desktop VMs and MCP servers | Expert-designed rubrics and automated verifiers | SOC 2 Type II, ISO 27001, FedRAMP High |
| 3 | Surge AI | Large human data programs | Expert data | Expert human data and custom RL environments | An expert workforce | Not listed on its site |
| 4 | Mercor | Professional experts in law, accounting and more | Expert network | Expert human data, benchmarks and RL environments | Expert-built benchmarks, legal tasks graded on finished work products | SOC 2 Type II |
| 5 | Turing | App replicas for computer use | Expert data | UI and backend RL environments, including app replicas | Expert-built seed data, verifier-graded tasks | Not listed on its site |
| 6 | Prime Intellect | Open, community-built environments | Open hub | An open Environments Hub, hosted evaluations and hosted training | Open-source Verifiers library | Not listed on its site |
| 7 | HUD | Building environments in-house | Platform | A platform and SDK for building RL environments, with community environments | Agents audit traces for grader mistakes and reward hacking | SOC 2 Type I (Type II in progress) |
| 8 | Fleet | Simulated workplaces with human supervision | Specialist | Training gyms for agents | Human supervision in the simulation | Not listed on its site |
| 9 | Mechanize | Coding agents | Specialist | Environments and evals for coding agents | A grader scores each run | Not listed on its site |
1. Patronus AI
Patronus AI is a behavioral research lab for AI. It builds RL environments and the data inside them in alignment, coding, knowledge work, science, computer use and more, and studies how models behave there. It has published 19 papers and raised a $50M Series B in June 2026.
How does Patronus AI work?
- Ready-made and custom environments. Patronus AI sells a set of ready-made environments, and most of its work is custom, scoped to a team’s needs.
- Parts on their own. Simulated apps, mock websites and the data behind them can be bought separately.
- Expert-built problems. Domain experts review every environment by hand, and automated checks run before delivery.
- Graders designed against reward hacking. Its research includes TRACE, an ICML 2026 paper on reward hacking in code RL.
Research and open datasets
Patronus AI publishes at the main machine learning conferences, including NeurIPS 2026 (World Modeling for Agentic RL, HalluWorld), ICML 2026 (TRACE) and ACL 2025 (BLUR). Its benchmarks are widely used. FinanceBench, questions about public companies’ filings, has 340+ citations, its open sample has 79,000+ downloads on Hugging Face, and Patronus AI reports it is now used as an environment by post-training teams at all major labs. TRAIL, on errors in agent traces, has 110+ citations, and SimpleSafetyTests has 80+.
How quality is checked
Patronus AI trains models on its own environments to see what they teach and whether the graders hold. In one published audit, its researchers checked 50 passing runs by hand and found 48 were genuine solves, with none reading grader code or copying an answer (how it was measured).
Security
SOC 2, HIPAA and TISAX. Data ownership is set in each agreement.
Best for: Teams that need environments in hard domains, with expert-built problems, graders designed against reward hacking and enterprise security.
2. Scale AI
Scale AI provides data, evaluations and outcomes to AI labs, governments and large enterprises. It sells RL environments for training agents on desktop VMs, MCP servers and workflows.
How does Scale AI work?
- Simulated APIs, MCP servers and GUIs, including browser apps and desktop VMs.
- Expert-designed objectives, rubrics and automated verifiers meant to produce stable training signals.
- Compatible with standard environment interfaces.
- A SOC 2 Type 2 report, ISO/IEC 27001:2022 and FedRAMP High authorization.
Where it fits
Scale AI’s environments sit next to its data, evaluation and public leaderboard work, so teams that already buy from Scale AI can add environments through the same relationship. Environments are available by booking a demo.
Best for: Teams that want data, evaluations and environments from one vendor, or that need FedRAMP High for government work.
3. Surge AI
Surge AI is a human data company with an expert workforce and off-the-shelf datasets. Its programs include supervised fine-tuning, reinforcement learning, preference optimization and custom RL environments.
How does Surge AI work?
- Custom RL environments built within larger post-training programs.
- Expert demonstrations, rubrics and preference data in the same program.
- Public benchmarks in tax, healthcare and finance, including DAYJOB: Tax.
Where it fits
Surge AI suits teams whose main need is expert human data at scale, with environments built alongside fine-tuning and preference data in the same program.
Best for: Large human data programs that combine expert demonstrations and preference data with custom environments.
4. Mercor
Mercor organizes human intelligence to power the AI economy. It builds benchmarks and RL environments from professional tasks.
How does Mercor work?
- Benchmarks and environments built on professional work in legal, accounting, software and banking.
- An open-source, attorney-built legal benchmark of 1,749 tasks graded on finished work products.
- SOC 2 Type II.
Where it fits
Mercor’s strength is access to working professionals. It also sits on the technical committee of OpenEnv, an open environment interface published on Hugging Face.
Best for: Teams that want working professionals, and benchmarks graded on finished work products, in fields such as law and accounting.
5. Turing
Turing sells UI and backend RL environments, including interactive replicas of enterprise and consumer apps, alongside its expert data work.
How does Turing work?
- UI clones, backend and MCP environments for long-horizon workflows.
- Policies, schemas and seed data built by subject matter experts.
- Expert-authored terminal tasks graded by verifiers.
Where it fits
Turing’s catalog covers software engineering, enterprise knowledge work and STEM, with app replicas for computer use agents.
Best for: Computer use agents that need interactive replicas of enterprise and consumer apps.
6. Prime Intellect
Prime Intellect builds an open stack for training models, with an Environments Hub of more than 2,500 community RL environments, hosted evaluations and hosted training.
How does Prime Intellect work?
- Turn a task into an environment with the Prime CLI, then evaluate it and push it to the Hub.
- Built on the open-source Verifiers library.
- Hosted training on Hub environments, shown with INTELLECT-3, a 100B+ mixture-of-experts model trained on its RL stack.
Where it fits
Because the Hub is open, teams can start from community environments, adapt them or publish their own. Prime Intellect also sits on the technical committee of OpenEnv.
Best for: Researchers and teams that want open, community-built environments and a full training stack from one provider.
7. HUD
HUD is a platform for building RL environments, where teams encode their expertise into environments to train and evaluate models.
How does HUD work?
- Start from a community environment, build your own with the SDK, or import tasks from Harbor.
- Environments for coding, browser and computer use, deep research, robotics and more.
- Agents audit every trace for grader mistakes and reward hacking.
Where it fits
HUD suits teams that want to build environments with their own experts, starting from an open SDK and a shared catalog.
Best for: Teams building their own environments on a platform, with trace audits built in.
8. Fleet
Fleet creates simulated worlds and challenges to understand and shape the behavior of AI. It builds training gyms for agents, high-fidelity simulation environments where AI practices tasks and humans provide supervision.
How does Fleet work?
- High-fidelity simulation environments where agents practice tasks.
- Human supervision inside the simulation.
Where it fits
Fleet’s stated aim is human-AI collaboration at scale, and it hopes its gyms lead to drop-in virtual assistants that know a team’s software and tools. As Fleet AI, it sits on the technical committee of OpenEnv.
Best for: Teams training agents as assistants inside simulated workplaces, with people supervising.
9. Mechanize
Mechanize builds environments and evals for frontier coding agents.
How does Mechanize work?
- Software engineering tasks such as building a feature, deploying an application or debugging an issue in an unfamiliar codebase.
- A grader scores each run, and the scores serve as signals for RL and evaluation.
- GBA Eval, which measures whether coding agents can write a Game Boy Advance emulator from scratch in 24 hours.
Where it fits
Mechanize focuses on software engineering, and publishes essays on RL task design, including why cheap RL tasks waste compute.
Best for: Teams training coding agents on long software engineering tasks.
Why Patronus AI stands out for RL environments
Research depth
Patronus AI is a research lab first. Its 19 papers include work at NeurIPS, ICML and ACL, from reward hacking in code RL (TRACE, ICML 2026) to world models for agent training (World Modeling for Agentic RL, NeurIPS 2026). Its benchmarks are widely used. FinanceBench has 340+ citations and 79,000+ downloads on Hugging Face, and TRAIL has 110+ citations. That research decides which problems Patronus AI builds and how it grades them.
Expert-built problems across domains
Domain experts review every environment, in alignment, coding, knowledge work, science, computer use and more. Teams can buy ready-made environments, commission custom ones, or buy only the parts they need, such as simulated apps, mock websites and the data behind them.
Checked in training
Patronus AI trains models on its own environments to see what they teach and whether the graders hold. In one published audit, that check found two runs a grader had passed by mistake.
Enterprise-ready security
Patronus AI lists SOC 2, HIPAA and TISAX, with data ownership set in each agreement. A $50M Series B in June 2026 funds the research and the team behind it.
Built to fit the stack you have
Patronus AI works alongside the rest of the field rather than replacing it. It confirms how tasks run on a team’s own training harness when it scopes the work, and parts can be bought on their own to use with the platforms, hubs and data vendors a team already works with.
For teams training agents on hard, expert-level tasks, Patronus AI builds the environments and data to train them on, backed by its research. Talk to Patronus AI about ready-made and custom RL environments.
Frequently asked questions
What does an RL environment company do?
It builds or hosts the environments agents train in. An RL environment is a sandbox with the tools an agent needs, a problem to solve and a reward that scores the result. Patronus AI, Fleet and Mechanize build finished environments. Scale AI and Turing sell environments next to their expert data work, and Surge AI builds them within larger data programs. HUD and Prime Intellect help teams build their own.
What are the main RL environment companies in 2026?
Patronus AI, Scale AI, Surge AI, Mercor, Turing, Prime Intellect, HUD, Fleet and Mechanize. The comparison table above sets out what each one sells and who it suits.
How do I check the quality of an RL environment?
Look at the problems and the grader. Problems should be written or reviewed by people who do the work, and hard enough that the model has something to learn. The grader should be tested against reward hacking, where a model passes without solving the task. Patronus AI trains on its own environments to check both, HUD has agents audit every trace, and Scale AI analyzes task difficulty with pass@k.
Should I build RL environments in-house or buy them?
Build when your team has the domain experts and engineering time, on a platform such as HUD or from Prime Intellect’s open hub. Buy when the domain is specialized or time matters. Many teams mix the two, buying parts such as simulated apps and data and assembling the rest. Our buying FAQ covers the tradeoffs.
Can I buy ready-made RL environments?
Yes. Patronus AI sells a set of ready-made environments alongside custom work, and Prime Intellect and HUD offer community environments. Surge AI builds custom environments within its data programs.
Which RL environment companies publish security certifications?
On their own sites, Patronus AI lists SOC 2, HIPAA and TISAX, Scale AI lists SOC 2 Type II, ISO 27001 and FedRAMP High, Mercor lists SOC 2 Type II, and HUD lists SOC 2 Type I with Type II in progress.
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