Home

DeepGym Wiki

Reward signals for RL code training. Sandbox it, verify it, score it.

Your model writes code. DeepGym runs it in an isolated sandbox (Daytona or local subprocess), checks it against a verifier, and gives you back a score you can feed straight into your training loop (TRL, verl, OpenRLHF, GRPO/DAPO/PPO).

---

Pages

Page	What's in it
Getting Started	Installation, first run, sandbox modes
Core API Reference	DeepGym, AsyncDeepGym, data models, exceptions
Environments	24 built-in envs, importable benchmarks, custom environments
Verifier Protocol	JSON output spec, writing verifiers, per-test rewards
Integrations	TRL, verl, OpenRLHF, lm-eval, HuggingFace Hub
CLI Reference	All CLI commands with options and examples
Sandbox Modes	Local, Daytona, Auto modes explained
Adversarial Testing	Reward hack detection, verifier auditing
API Server	REST API, authentication, endpoints
Advanced Usage	Gymnasium wrapper, multi-turn, shaped rewards, batch GRPO
Configuration	Environment variables, timeouts, directories
Architecture	System design, data flow, module map

---

Links

• GitHub: DeepGym/deepgym
• PyPI: pip install deepgym
• License: MIT
• Python: >= 3.10