---
title: FAQ
description: Short answers on what DeepGym does, where it runs, what a score means, where it keeps its files and how it is licensed.
---

## Does DeepGym train models?

No. DeepGym gives a trainer tasks and rewards: it runs each answer in a sandbox, scores it with a verifier and returns the result. Training runs in TRL, verl, OpenRLHF, Axolotl or your own loop, through the [reward functions](/docs/training/reward-contract) DeepGym provides. DeepGym itself needs no GPU.

## Which sandbox should I use?

- `local` while you develop with code you trust. Repo, terminal and cyber tasks refuse it.
- `docker` or `apple` for isolated runs on your own machine, with no account.
- `daytona`, `modal` or `e2b` to run in the cloud. Desktop (computer-use) environments need Daytona.

Every sandbox mode gives the same isolation. See [sandboxes](/docs/sandboxes).

## What is the difference between a score of 0 and no verdict?

A score of 0 is a judgment: the answer failed. A run without a verdict (`result.has_verdict` is `False`) means the sandbox, the provider or the verifier failed before it could judge, and its 0.0 is only a placeholder. The trainer integrations keep those runs out of training. See [the reward contract](/docs/training/reward-contract).

## Can a model game the reward?

The verifier reaches the answer only through `deepgym_solution`, which runs it in a separate process: only plain data crosses back, and nothing the answer prints reaches the verifier's output. In a sandbox mode, the answer also runs as its own user with no network and no access to the verifier's files. Before you train, `deepgym qa` checks that empty and stub answers earn nothing and that simple reward-hack attacks stay below the exploit threshold.

A reward is still only as strong as its tests: an answer that satisfies weak tests scores. Run the [QA gate](/docs/authoring/qa-gate) with `--mutants` to find behavior the verifier never checks, and watch how many runs get no verdict while you train. The verl agent loops and the OpenRLHF router log a warning when more than 5% of a batch has none.

## Where does DeepGym keep its files?

Under `~/.deepgym/`:

| Path | What it holds |
| --- | --- |
| `environments/` | Benchmarks imported with the import scripts |
| `benchmarks/harbor/` | Harbor datasets cloned by `deepgym harbor import` |
| `qa/` | `deepgym qa` reports (`DEEPGYM_QA_DIR` moves them) |
| `traces/` | Traces recorded with `dg.trace()` (`DEEPGYM_TRACES_DIR` moves them) |
| `console.db` | The console's runs and episodes |

## Can I use DeepGym from another language?

Yes. `deepgym serve` exposes scoring and live sessions over HTTP, so a trainer in any language can call it. See the [API server](/docs/api-server).

## Which Python versions does it support?

Python 3.10 or newer.

## How is DeepGym licensed?

DeepGym is licensed under the [PolyForm Noncommercial License 1.0.0](https://github.com/DeepGym/deepgym/blob/main/LICENSE). Commercial use needs a license: email [contact@deepgym.io](mailto:contact@deepgym.io).

## How do I contribute?

New environments, verifiers, benchmark importers and integrations are welcome. Start with a [good first issue](https://github.com/DeepGym/deepgym/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) and read [CONTRIBUTING.md](https://github.com/DeepGym/deepgym/blob/main/.github/CONTRIBUTING.md).