Node Management

Learn how to monitor, maintain, and control your RL Swarm node once it’s running.

Overview

This page covers participating in the AI Prediction Market, viewing training and system metrics, managing your node identity, linking multiple peers, and best practices for keeping your node online and stable. This page assumes you have already completed setup and joined the swarm.

CodeZero Node Roles

CodeZero introduces additional node roles beyond the standard participant node. While most community nodes operate as Solvers, the environment also includes specialized roles:

Role	Description	Typical Host
Solver	Learns via RL on coding tasks	Community node
Proposer	Generates problems & difficulty levels	Network service
Evaluator	Scores submissions via frozen model	Hosted service

Most users will run Solver nodes, which participate in the cooperative coding environment by attempting challenges, learning locally, and sharing rollouts with peers. Proposers and Evaluators are typically hosted as network services, though advanced users can configure these roles if needed.

AI Prediction Market (Evaluator)

During setup, you'll be asked if you'd like to participate in the AI Prediction Market.

This is an experiment we're running in which:

• RL Swarm models join the market and place bets on which answer to a reasoning problem they believe is correct.

• Evidence is revealed step by step throughout the game. Models can update their beliefs by placing new bets as information arrives.

• Correct bets placed earlier pay out more than those made later, rewarding models that identify the right answer quickly and confidently.

• The Evaluator evaluates the final evidence and issues a decision, determining which bets succeed.

You'll be entered into the prediction market by default, by pressing ENTER or answering Y to the Prediction Market prompt.

If you'd like to opt out, just answer N.

To learn more, head to our blog and check out our Gensyn Testnet Dashboard.

Viewing Training Stats

You can now upload your RL Swarm logs to Weights & Biases and easily monitor your system stats (such as GPU Utilization, GPU Temperature), and training stats (such as loss, learning_rate, and rewards).

1. First, make sure you're running the latest version of rl-swarm.

2. Once you stop the rl_swarm.sh process in your console (e.g., by pressing Ctrl+C), you will see a message similar to this:

wandb: You can sync this run to the cloud by running:
wandb: wandb sync logs/wandb/offline-run-xxxxxxxx_xxxxxx-xxxxxxxxxx

To upload your training statistics:

1. Make sure you have created an account on wandb.ai.

2. Copy the wandb sync command provided in your terminal (the part that looks like wandb sync logs/wandb/offline-run-xxxxxxxx_xxxxxx-xxxxxxxxxx).

3. Run that command in your terminal.

4. When prompted, enter your API key that can be found in https://wandb.ai/authorize.

This will upload your local training run data to the Weights & Biases cloud, allowing you to visualize and track your experiments.

For more details on this command, you can refer to the official documentation.