---
title: "OpenEnv"
description: "2 posts about OpenEnv, summarised, each linking to the original."
---

# OpenEnv
> 2 posts about OpenEnv, summarised, each linking to the original.

## Articles

### [Welcome RL Environments to the hub](https://yomu.fyi/post/welcome-rl-environments-to-the-hub.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: ben burtenshaw, Florian, Ryan Marten, merve, Quentin Lhoest, Julien Chaumond
- Published: Sep 28, 2026
- Type: Announcement

Hugging Face Hub now has a dedicated RL Environments filter for dataset repositories tagged with rl-environment, addressing the problem of siloed environments across frameworks like Harbor, Verifiers, OpenEnv, and NeMo Gym. Instead of building a new registry, the Hub uses existing dataset repos with framework tags that generate loading snippets, so an environment consists of tasksets stored as datasets and runtimes provided by frameworks. The environment gives an agent a task, responds to actions with observations, and scores the outcome with rewards for evaluation or training, and users can run environments by pulling the dataset and using the framework’s runtime and verifier code. The release includes pre-tagged environments such as BeyondSWE, Terminal-Lego, Multi-SWE-RL-Verified, and others, with run commands provided for each framework. Future plans include per-config snippets and automatic framework tagging on upload, and the Hub already stores data, versions it, gates it, and serves it to millions.


### [Training a coding model to paint watercolours with TRL and OpenEnv](https://yomu.fyi/post/training-a-coding-model-to-paint-watercolours-with-trl-and-openenv.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Sergio Paniego
- Published: Sep 3, 2026
- Type: Explainer

The project reproduces an earlier idea: training a language model to write JavaScript that paints watercolours with p5.brush, using TRL and OpenEnv. Its open pipeline includes a hand-rated reference pool, an RL environment, training scripts, trained models, and Hugging Face Jobs, Spaces, Inference Providers, and Hub artifacts. The environment renders sketches in headless Chromium, restricts the model to ten library methods, rejects cheats through a gate, and combines gate, length, pairwise-judge, and HPSv3 rewards. Three reward mixes were trained: judge-led (0.60/0.30), hps-led (0.30/0.60), and hps-only (0.00/0.90); the first two ran to step 110, while validation stopped at step 60. The post reports that both pairwise-judge runs learned and identifies the hand-rated pool as the bottleneck.