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title: Reasoning Core Environment Server
emoji: 🧠
colorFrom: green
colorTo: blue
sdk: docker
pinned: false
app_port: 8000
base_path: /web
tags:
- openenv
- agent-environment
- reasoning
- reinforcement-learning
- evaluation
- symbolic-reasoning
---
# Reasoning Core Environment
An [OpenEnv](https://github.com/huggingface/openenv) environment for formally
verifiable symbolic reasoning across logic, mathematics, planning, syntax, and
related procedural domains.
Tasks come from
[`reasoning-core/formal-reasoning-env`](https://huggingface.co/datasets/reasoning-core/formal-reasoning-env)
and are scored by the task-specific evaluators in
[`reasoning-core`](https://github.com/sileod/reasoning_core).
## Use The Hosted Environment
```python
from reasoning_core_env import ReasoningCoreAction, ReasoningCoreEnv
with ReasoningCoreEnv(
base_url="https://reasoning-core-reasoning-core-openenv.hf.space"
) as env:
result = env.reset(split="train", seed=42, size=1000)
print(result.observation.prompt)
result = env.step(ReasoningCoreAction(answer="<answer>...</answer>"))
print(result.reward)
```
Each episode has one action:
1. `reset()` returns a symbolic reasoning prompt.
2. `step(ReasoningCoreAction(answer=...))` scores the answer and ends the episode.
Plain answers and answers wrapped in `<answer>...</answer>` are accepted. Rewards
are task-specific scores in the range 0 to 1.
The environment only serves pre-generated examples from the Hugging Face
dataset. Rows whose task scorer is unavailable in the installed
`reasoning-core` version are skipped, preventing deprecated or unsupported task
types from reaching an episode.
## Local Development
```bash
uv sync
uv run openenv validate
uv run openenv build -t reasoning-core-openenv
```
Run without Docker:
```bash
uv run server
```
The service exposes the interactive UI at `/web`, API documentation at `/docs`,
health information at `/health`, and the persistent environment API at `/ws`.
## Citation
If you use this environment, cite the Reasoning Core paper:
```bibtex
@article{reasoningcore2026,
title={Reasoning Core: A Scalable Procedural Data Generation Suite for Symbolic Pre-training and Post-Training},
author={Lacombe, Valentin and Quesnel, Valentin and Sileo, Damien},
journal={arXiv preprint arXiv:2603.02208},
year={2026},
url={https://arxiv.org/abs/2603.02208}
}
```
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