The Gym Where AI Models Get Stronger
Training and eval infrastructure for AI models and agents. Code, vision, tool-use — run real tasks in sandboxed environments and get rewards back.
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Most teams build this from scratch
Code model teams often spend months building sandbox infrastructure, test harnesses, and evaluation pipelines. Then they maintain it indefinitely. We handle the infra so you can focus on training.
Sandbox Setup
Weeks of Docker config, security hardening, and resource management before you can run a single episode.
Test Harness Maintenance
Test suites drift, edge cases multiply, and reward signals degrade. Keeping evaluation reliable is a full-time job.
Scaling Bottleneck
Running millions of parallel episodes needs orchestration, monitoring, and failover. Most teams cap at hundreds.
Scaling Bottleneck
Running millions of parallel episodes needs orchestration, monitoring, and failover. Most teams cap at hundreds.
Three ways to use DeepGym
From training runs to enterprise evaluation.
Train
Run RL training loops against sandboxed code environments. Real repos, real test suites, pass/fail reward signals. Scale to thousands of parallel episodes.
Evaluate
Benchmark your code model against curated task sets. Measure pass rates, execution time, and correctness across difficulty levels.
Enterprise
Private deployments, custom task libraries, and dedicated infrastructure for teams that need control over their evaluation pipeline.
Code models need better training infrastructure
The number of teams building coding agents is exploding. The infrastructure layer is missing.
Agent Explosion
More teams are building AI agents every month. They all need evaluation infrastructure.
Reproducibility Crisis
Without standardized environments, teams can't compare models or reproduce results.
Infra Gap
Compute is commoditized. Training frameworks exist. The missing piece is managed execution and evaluation.
Infra Gap
Compute is commoditized. Training frameworks exist. The missing piece is managed execution and evaluation.
Built by people who train models
We've spent years in GPU programming, benchmark design, and RL infrastructure.
Execution-First
Every environment runs real code in real containers. No mocks, no simulations, no shortcuts.
Battle-Tested Harnesses
Test suites designed to catch reward hacking. Adversarial probing built into every environment.
Language Agnostic
Python, TypeScript, Go, Rust. Write environments in whatever your team uses.
Secure by Default
Full OS-level isolation. Untrusted agent code can't escape the sandbox.
Observable
Every episode logged. Every reward signal tracked. Debug training runs, not infrastructure.
Fast
Optimized for low-latency environment stepping.
Train, Evaluate, and Benchmark AI Models
Run execution-based RL, benchmark agents, and measure performance across code, vision, and tool-use tasks.