# RL Swarm (Paused)

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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, explore [Delphi](https://app.gitbook.com/s/f4Fc1kUotFaHBeh5Gs8A/delphi/what-is-delphi), the first live prediction market for machine intelligence.
{% 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="https://1034405018-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FIcazOdplbOmP4R0T7sG8%2Fuploads%2FLcwS5rbU3BY6pBzp21Ga%2Fimage.png?alt=media&#x26;token=998b1204-fbdc-4f03-a640-529ef43287d1" 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.](https://docs.gensyn.ai/testnet/broken-reference)

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](https://docs.gensyn.ai/testnet/broken-reference), **\[1]** execution, **\[2]** verification, **\[3]** communication, and **\[4]** coordination, work together in a live environment.

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RL Swarm forms the foundation of Phase 0 of the Gensyn Testnet, providing the first public demonstration of decentralized AI collaboration in action.
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#### 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.
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{% 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](https://docs.gensyn.ai/testnet/rl-swarm/getting-started) section and select your platform for OS-specific set-up guides, or browse our [Troubleshooting](https://docs.gensyn.ai/testnet/rl-swarm/troubleshooting) documentation.