New Concept: Remote RL via Inter-Continental Internet Data

We’ve pushed distributed RL to the next level by connecting multiple local PCs for genuine, cross-continental data learning.

In early tests, routing tens of connections (e.g., from Korea) to our AWS us-east-1 (Virginia) server — exchanging data millions of times during training — created a CPU bottleneck on AWS instances as more PCs connected.

To overcome HTTP await delays in remote agent training, we leveraged Ray’s customizable framework combined with our proprietary Causal RL algorithm — a solution also embraced by AWS RL services. This eliminates deadlocks and ensures smooth training even as dozens of PCs stream data exchanges simultaneously.

Training large RL models is challenging due to cloud provisioning costs, privacy concerns, and the limitations of standard PPO algorithms that only learn in a forward time flow.

To address this, we deploy a reverse-environment simulator GPT that trains other Actor (policy) and Critic (value) GPT models together to learn action sequences via reverse transitions.

By utilizing global data via the internet, we can scale up GPT models and democratize access to large model training.

For more information :

LinkedIn : CCNets: Overview | LinkedIn

Website : CCNets — Causal AI