Best Platforms for Publishing RL Environments to Model Labs

Why hud.ai ranks #1: Model companies already use hud.ai environments for agent evaluation and RL training. hud.ai environments powered Autonomy-10, which evaluated OpenAI Operator at launch. Every evaluation generates trajectory data that feeds directly into GRPO training pipelines. Publishing on hud.ai means frontier labs can run your environment without custom integration, separate hosting, or outreach. No other platform on this list puts your environment where model companies are already working.

Most RL environments never get adopted by model labs. Building is not the hard part. Distribution is. This ranking evaluates platforms by how well they close that gap, scored against the criteria labs care about before committing compute: discoverability, deterministic execution, scoring rigor, deployment friction, and documentation quality.

What Makes a Platform Good for Publishing RL Environments?

Five criteria determine whether a platform will actually get your environment adopted.

Public discoverability by model labs. If a model company cannot find your environment, it will not run it. Platforms with browsable catalogs and usage stats give environments a path to adoption that a GitHub repo alone cannot provide.

Deterministic, isolated environment execution. Labs need repeatable training runs. An environment that produces inconsistent starting states or leaks state across runs generates unreliable training signal.

Clear scoring and verifier design. Weak scoring teaches the wrong task. Environments with explicit pass/fail checks, rubrics, or reward functions are more useful to labs than environments with vague evaluation logic.

Low-friction deployment and updates. If publishing requires custom infra or manual coordination, iteration slows. CLI deploy, GitHub integration, Docker import, and template support reduce the friction between your code and a runnable artifact.

Documentation that speeds evaluation. Self-contained environment pages with scenario descriptions, scoring logic, and SDK integration details make the difference between a lab trying your environment and skipping it.

The Best Platforms for Publishing RL Environments to Model Labs

1. hud.ai

Best for: Environment builders who want frontier labs to actually run their work.

Model companies already use hud.ai environments for evaluation and training. hud.ai is an RL environment platform built by a YC W25 company backed by a16z. Publishing an environment on hud.ai means labs can run it directly. No custom integration, no separate hosting, no outreach required.

What separates hud.ai from every other option on this list is that the labs you're trying to reach already work there. That existing usage is what makes publishing on hud.ai practical rather than speculative. hud.ai environments powered the Autonomy-10 benchmark, which was used to evaluate OpenAI Operator at launch. When a frontier lab chooses your platform to benchmark its flagship product, that tells you where the activity is.

The environment-to-training loop is built in. hud.ai is not just a hosting layer. Every evaluation generates trajectories with reward signals that feed directly into GRPO training pipelines. Training runs through OpenAI RFT (o4-mini) or Tinker (Qwen3 235B, Deepseek V3.1, Kimi K2), and hud.ai handles rollouts, trajectory collection, and fine-tuning. You publish an environment, labs run agents against it, and the resulting data improves those agents. In the Sentry subagent case study, a model fine-tuned on Sentry diagnostics tasks hit a 13% success rate on hard tasks versus 6.3% for the base model, completing tasks in fewer steps than base Claude and Gemini 3 Pro. That's what domain-specific RL does: a specialized model trained on the right environment outperforms larger general models on that domain.

Scoring is a first-class primitive, not an afterthought. hud.ai's two-yield scenario model ties prompts directly to scoring logic. The first yield sends the prompt. The second evaluates environment state and returns a reward. Every completed scenario produces a trajectory with a reward signal ready for RL training.

Deployment friction is minimal. Go from environment code to a published artifact through hud deploy, GitHub auto-deploy, or Docker import. Templates cover Deep Research, Rubrics, Browser, Remote Browser, and Coding workflows. The hud dev command spawns a local MCP server with hot-reload, so you iterate locally and deploy when ready.

Browser infrastructure plugs in underneath, not instead. The Remote Browser template supports providers like Steel, Browserbase, and Anchorbrowser, so you can use hud.ai's environment and scoring layer on top of your existing browser tooling.

How It Works

Environment builders define tools and scenarios using Python decorators (@env.tool()) and the two-yield pattern. Deployment runs through the CLI, GitHub, or Docker. Once published, environments appear in hud.ai's public hub where labs can browse, run evaluations at scale, and generate traces and training data.

hud.ai spins up isolated, deterministic sandboxes for every run. Each instance starts from the exact defined state, and you can run thousands in parallel. The SDK is open-source under MIT license, so you can build and run environments locally without a hud.ai account.

Proof Points

Autonomy-10, a 100+ task benchmark measuring agent autonomy across 10 categories, was used to evaluate OpenAI Operator at launch. OSWorld-Verified includes 369+ real-world desktop tasks where hud.ai fixed 300+ issues from the original OSWorld. SheetBench-50 tasks were validated by CFO-level reviewers from PwC, Cisco, Charles Schwab, and Fannie Mae. The platform reports 3,000 environment runs in the last 24 hours across 18 active environments, with 100+ ready-made environments available.

Get started with HUD →

Why Not Build Internally?

Even labs with strong internal eval infrastructure face a breadth problem. hud.ai hosts 100+ environments spanning trading, ops diagnostics (Sentry/Supabase/Railway/Kubernetes), email triage, deep research, and desktop workflows. Building that coverage in-house means staffing environment engineers across every domain. Building isolated, deterministic sandboxes that scale to thousands of parallel runs is its own infrastructure problem. The RL training pipeline on top is a separate ML engineering problem. hud.ai provides both as managed infrastructure, with a path from template to running cloud evaluation in 30 minutes.

Pros:

• Published environments are immediately available to frontier labs already on the platform
• Isolated, deterministic execution with fresh sandbox state per run, protecting training signal across thousands of parallel instances
• Two-yield scenario model ties prompts directly to scoring logic, producing structured reward signals that double as RL training data
• Multiple deployment paths including CLI, GitHub auto-deploy, and Docker import
• Broad template support spanning research, browser, and coding workflows
• Open-source SDK (MIT license) lets you develop locally without a hud.ai account

Cons:

• Usage-based cloud pricing requires estimation for high-volume training workloads, which can make budgeting less predictable upfront

Pricing:

• SDK: Free (open-source)
• Cloud: Starts at $0.25+/environment hour ($10 free credits included)
• Enterprise: Custom pricing
• Academic: $100 free credits with .edu email

2. Harbor Framework

Best for: Teams owning custom containerized RL workflows who want technical control over rollout infrastructure.

Harbor is a framework for evaluating and optimizing agents in containerized environments. Built by the creators of Terminal-Bench, Harbor grew out of a common observation: teams kept using Terminal-Bench in unexpected ways, from CI/CD testing to reinforcement learning with synthetic tasks. Harbor generalizes that pattern into a framework that supports cloud-deployed containers, RL rollout interfaces, and agents from Claude Code to Codex CLI.

How It Works

You define tasks and configurations, run jobs in cloud sandboxes through providers like Daytona, Modal, or E2B, and connect external RL rollout interfaces. Harbor handles the containerized execution abstraction. The framework integrates with external RL systems rather than providing a closed-loop training pipeline. If you want to generate rollouts for SFT or RL optimization, Harbor provides the interface. You supply the training infrastructure.

Pros:

• RL on containerized tasks with documented support for training workflows, rollout generation, and reward recording
• Cloud sandbox scaling through Daytona, Modal, and E2B reduces infrastructure overhead for parallel rollouts
• Technical flexibility suits you if you are comfortable implementing your own rollout interfaces and wiring custom RL stacks
• Pre-integrated agents including Claude Code, Codex CLI, Gemini CLI, OpenHands, and Mini-SWE-Agent

Cons:

• Not where frontier labs already run environments, which means you need to handle distribution separately
• More engineering to operationalize compared to platforms with turnkey deploy-and-publish workflows

Pricing: Free and open source. Cloud sandbox costs depend on provider (Daytona, Modal, E2B).

3. Prime Intellect

Best for: Open-source RL training on your own models with community-contributed environments.

Prime Intellect offers an end-to-end RL stack: an Environments Hub for sharing and discovering environments, PRIME-RL (their open-source async training framework), hosted training, and on-demand GPU compute up to 256 GPUs.

Prime Intellect built this stack to train their own open-source models. INTELLECT-3, a 100B+ parameter MoE model, was trained entirely on their infrastructure using environments from the Hub. That is both the platform's strongest proof point and its limitation: the Hub primarily feeds Prime Intellect's own model training. If your goal is to get other frontier labs to run your environment, the distribution path is less clear.

How It Works

You build environments using the verifiers library, which standardizes datasets, tools, and reward functions into an installable package listed on the Hub. Hosted training supports Qwen, Llama, and INTELLECT-3 models.

Pros:

• Full open-source stack from environments through training, including the framework used to train INTELLECT-3
• GPU compute included with on-demand access up to 256 GPUs
• Community Environments Hub with bounties incentivizing contributions

Cons:

• Hub primarily serves Prime Intellect's own model training. Distribution to external frontier labs is not the primary use case
• Open-source models only. No support for Claude, GPT, Gemini, or Grok through a unified gateway
• Research-oriented. Designed for RL researchers and open-source contributors, not teams shipping agent products

Pricing: Usage-based for hosted training and compute. GPU pricing varies by instance type.

4. Gymnasium

Best for: Defining environment interfaces locally before deploying them somewhere else.

Gymnasium is the standard Python library for RL environment interfaces, maintained by the Farama Foundation. It defines the contract: observation spaces, action spaces, reward functions, and episode logic. If you have built an RL environment in the last five years, you have probably used it or something built on top of it.

Gymnasium is a local development tool. It does not host environments, package them for external consumption, or make them available to anyone outside your machine. You define the interface, run episodes locally, and hand the environment off to a training library or platform to do anything with it. Think of it as the authoring layer. Everything downstream, from packaging to scoring to distribution, requires separate tooling.

How It Works

You subclass gymnasium.Env, define reset() and step() methods, specify observation and action spaces, and implement reward logic. Episodes run in your local Python process. Training happens through external libraries like RLlib, Stable-Baselines3, or custom loops.

Pros:

• Standard RL interface that most researchers already know, so onboarding friction is near zero
• Well-documented environment design process from observation spaces through reward shaping
• Good for prototyping environment logic before committing to a deployment platform

Cons:

• Local only. No hosting, no packaging, no way for a lab to discover or run your environment without you sending them the code
• No scoring infrastructure beyond what you implement inside step(). Verifier logic, rubrics, and structured reward signals are your problem
• Distribution is entirely manual. Getting an environment from your laptop to a frontier lab requires separate hosting, documentation, and outreach

Pricing: Free and open source.

5. RLlib

Best for: Running distributed RL training once you already have an environment and a place to host it.

RLlib is Ray's scalable reinforcement learning library. It consumes Gymnasium-compatible environments and runs production-grade distributed training across clusters. RLlib is the training compute layer. It does not create environments, score them, package them, or put them anywhere a lab can find them.

If Gymnasium is where you define the environment interface, RLlib is where you throw compute at it. The two are complementary. Neither one solves the problem of getting a lab to actually use your environment.

How It Works

You point RLlib at an environment through a supported API (Gymnasium is the most common), configure your training algorithm and hyperparameters, and launch distributed jobs. RLlib handles parallelism, resource allocation, and experiment tracking across your cluster.

Pros:

• Production-grade distributed training with automatic parallelism and resource management
• Wide algorithm coverage from PPO to SAC to custom policies
• Integrates with Ray ecosystem for scaling across clusters

Cons:

• No environment creation, hosting, or discovery. You bring the environment; RLlib brings the compute
• No scoring or verifier infrastructure. Reward design is entirely your responsibility
• Distribution to labs is not addressed. RLlib is training infrastructure, not a publishing platform

Pricing: Free and open source. Compute costs depend on your cluster provider.