Astra by HackerRank

RL environments and expert data for frontier AI labs

Astra creates realistic engineering tasks, reproducible environments, robust verifiers, and expert-reviewed rollout data for training and evaluating frontier coding models.

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Designed forFrontier AI labs
Work horizonMulti-hour tasks
Built forRL training + evaluation

Our offerings

Build the RL loop, from environment to reward

Each program connects realistic expert work, robust verifiers, repeated rollouts, and expert review around a frontier lab’s training or evaluation objective.

Long-horizon RL environments

Give coding agents realistic, multi-step work that takes several hours and requires planning, implementation, testing, debugging, and validation.

What you receive

  • Real-world tasks with clear outcomes and meaningful technical decisions left to the agent
  • Reproducible environments with the required codebase, infrastructure, tools, browser, test data, and local assets
  • Resettable state and consistent execution across training runs and capability evaluations

Verifiers and reward design

Measure working behavior across different valid implementations, award meaningful partial credit, and reduce opportunities for reward hacking.

What you receive

  • Deterministic checks for tasks where critical interfaces can be defined in advance
  • Implementation-flexible evaluation for tasks where agents can make their own architecture, API, data model, and user experience decisions
  • A scalar reward, capability-level results, and evidence showing what worked and what failed

Private model evaluations

Compare how models perform on capabilities that matter to your training, research, or product goals.

What you receive

  • Repeated rollouts across selected models, reasoning settings, agent harnesses, and tool configurations
  • Results covering reward, completion, consistency, runtime, cost, and common failure patterns
  • Trajectories, checkpoints, logs, and failure evidence explaining where models succeeded, took shortcuts, or stopped

Rollout data and expert review

Turn model attempts into structured feedback and training data reviewed by people who understand the underlying work.

What you receive

  • Successful and unsuccessful rollouts reviewed by qualified domain experts
  • Identified mistakes, corrected approaches, working solutions, code reviews, and step-level feedback
  • Structured trajectories and annotations delivered in the format required by your training pipeline

RL environment lifecycle

From target capability to calibrated reward

Astra covers the full evaluation lifecycle: task design, reproducible infrastructure, verifier development, calibration, repeated rollouts, and expert review.

  1. Define the capability

    Define the agent capability, RL training or evaluation objective, operating constraints, and evidence your lab needs.

  2. Build the environment

    Domain experts and environment engineers create realistic tasks, portable workspaces, reference outcomes, and reliable verifiers.

  3. Verify and calibrate

    Complete, partial, incorrect, unsafe, and independently built solutions test whether the verifier is fair, repeatable, and difficult to game.

  4. Run and review rollouts

    Repeated rollouts produce expert-reviewed findings for research and training decisions.

The HackerRank advantage

Built on HackerRank's global developer community

Astra can source and qualify experts from HackerRank's developer community based on the technologies, domains, and evaluation work required for each program. Experts can be qualified through realistic work samples and paid trial projects before joining a program.

HackerRank platform scale

2,500+
companies
31M+
developers
9,000+
questions
53M+
attempts processed
Join the expert network
An expert contributing to a frontier-agent evaluation program

Matched by domain and technical expertise

Scope an RL environment or evaluation

What capabilities do you want to improve or evaluate?

Share the target capability, agent harness, rollout constraints, and RL training or evaluation objective. We will shape the environment, verifier, private evaluation, or data program around it.

  • 01Target capability or domain
  • 02Agent harness or models
  • 03RL training or evaluation objective