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HUD turns raw data and expert knowledge into reinforcement learning tasks. A task asks an agent to do something inside an environment and grades the result. HUD hosts the environment, runs models on your tasks, records every attempt as a replayable trace, and reviews traces with QA agents so you know each reward measures what the task intended.

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Choose your path

Create tasks without code

Turn your files and expertise into graded tasks in the web app.

Bring Harbor tasks

Import a Harbor task and its runs, then find what’s broken with QA.

Build with the SDK

Write environments in Python, deploy them, evaluate, and train.

Review with QA agents

Catch reward hacking, grader mistakes, and prompt gaps in every trace and task.

Work as a team

Coordinate many task authors with review stages, scoped access, and QA.
Beyond text and code, agents on HUD can operate a browser or desktop or control a robot in simulation. New to HUD? The quickstart creates, runs, and reviews a task in the web app in about ten minutes.

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How HUD works

  1. Create tasks. Write them in the web app, import them from Harbor, or define them in Python with the SDK. A task can be anything its environment can grade, from fixing a failing test to picking up an object in simulation. Tasks are grouped into a taskset.
  2. Run them. An evaluation job runs every task on one or more models, several times each. Every attempt is a trace with its full trajectory and reward.
  3. Review them. QA agents read traces and tasks and flag rewards that cannot be trusted: reward hacking, passing work that is wrong, failing work that is right, prompts the grader disagrees with, and the cause of each failure.
  4. Improve and ship. Fix what QA flags, move tasks through review, then publish the taskset as a benchmark or train a model on it.
hub-environments

Environments

The worlds agents act in, and the task templates they expose.
hub-tasksets

Tasksets

Benchmarks and training sets, with the authoring and review around them.
hub-evaluations

Evaluations

Run tasksets across models and replay every graded attempt.

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Trust every reward

A reward is only useful if it measures what the task asks for. QA agents are reviewers that HUD runs for you. Each one answers one question about a trace or a task and returns a verdict with findings that point at the steps or files involved. Run them on any completed trace or task, or turn them on for a taskset so every new trace is reviewed. A review is billed from your credits like a short run: the model usage of the analysis and the runtime of the environment it runs in.

QA agents

What each QA agent checks, with examples.

Run QA

Review one trace, a whole taskset, or every new run automatically.

Read results and fix tasks

Turn findings into task fixes.

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Platform features

Data

Upload files once and attach them to any task.

Models

Evaluate catalog models and your team’s trainable forks.

Training

Train your team’s models on your tasks from Python.

File tracking

See the files an agent changed at every step.

Projects

Separate work and grant scoped access inside a team.

Team and members

Invite members, set roles, and manage settings.

REST API

Automate tasks, runs, and QA over HTTP.

Platform MCP

Let coding agents read environments, tasksets, jobs, and traces.