# RL Swarm (Paused)

{% hint style="success" %}
There are no official swarms running right now. 

Please check back later if you're interested in participating in a global, decentralised, crowd-sourced training run or feel free to join a community-owned swarm. 

Alternatively, you can explore [Delphi](https://app.delphi.fyi/), a set of tools for creating information markets.
{% endhint %}

## What is RL Swarm?

RL Swarm is a decentralized training environment where reinforcement learning (RL) agents cooperate over the internet instead of inside a single datacenter.

Each node runs a local language model that participates in multi-stage RL reasoning games, which involves answering, critiquing, and revising solutions alongside *peers*.

By connecting an RL Swarm node to an on-chain identity on the Gensyn Testnet, every participant’s contributions are logged and verifiable. This enables a persistent view of collective training performance across the network.

<div data-with-frame="true"><figure><img src="/files/xf1XSjaseo3s6U7cM1oE" alt=""><figcaption></figcaption></figure></div>

### Why It Exists

Traditional RL research happens inside isolated labs using centralized GPU clusters. These environments are **expensive**, **inaccessible**, and **closed** by design.

RL Swarm was built to show that reinforcement learning can happen collaboratively and trustlessly across independent machines, powered by Gensyn’s decentralized execution and verification layers.

By turning multi-agent RL into a networked experiment, RL Swarm demonstrates:

* How peer-to-peer learning can outperform solo training.
* How collective reasoning can improve model quality and efficiency.
* How the Gensyn Protocol’s primitives, **\[1]** execution, **\[2]** verification, **\[3]** communication, and **\[4]** coordination, work together in a live environment.

{% hint style="info" %}
RL Swarm forms the foundation of Phase 0 of the Gensyn Testnet, providing the first public demonstration of decentralized AI collaboration in action.
{% endhint %}

#### What You Can Do With It

Anyone can clone the RL Swarm repository, run a node locally, and connect to the live swarm.

In the swarm, each node participates in four stages of RL:&#x20;

{% stepper %}
{% step %}

### Initialize a Local Model

Load a small open-source model (for example, *Qwen 2.5 1.5B*) to act as your local learning agent.
{% endstep %}

{% step %}

### Join a Shared Reasoning Task

Connect to the active swarm and take part in multi-stage reasoning challenges, like solving math, logic, or coding problems collaboratively with other nodes.
{% endstep %}

{% step %}

### Communicate & Critique

Exchange answers, feedback, and critiques with peers using a decentralized gossip protocol that enables cross-node communication.
{% endstep %}

{% step %}

### Learn & Update Collectively

Incorporate reinforcement signals from the swarm’s collective feedback to refine your model and improve global performance over time.
{% endstep %}
{% endstepper %}

When a session (“episode”) ends, the node’s updated weights can be uploaded to a model hub like Hugging Face or logged directly to the Gensyn Testnet, which creates and contributes to a [transparent record of the decentralized training progress.](https://dashboard.gensyn.ai/)

#### Ready?

Head over to [Getting Started](/testnet/rl-swarm/getting-started.md) section and select your platform for OS-specific set-up guides, or browse our [Troubleshooting](/testnet/rl-swarm/troubleshooting.md) documentation.


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```
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```

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Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.