Most RL pipelines train a brand-new agent for each task, spending huge amounts of data and compute and then throw away those learned weights once you switch environments. Why train AI once when it could learn forever? Gensyn’s BlockAssist proves decentralized continuous RL in a live app, an AI companion in Minecraft, that learns from your gameplay on your device and then improves collectively via a permissionless compute swarm. In contrast, most large models are fine-tuned with human feedback or massive labeled datasets in a centralized pipeline. By contrast, BlockAssist flips the script. Learning happens from each user’s gameplay stream itself, continuously and privately. - For one, it removes the dependence on massive offline training runs for every new capability. The model refines itself on the fly. - It’s also inherently personalized: my BlockAssist might develop different skills than yours because we each train it on our own gameplay. We are essentially crowd-sourcing AI improvement in a way that scales with users, which is a very different paradigm from centralized AI fine-tuning. Also, we are notorious (especially in crypto) talking up infrastructure and its potential, but Gensyn is proving it with a real app. BlockAssist is a major milestone showing what’s possible when RL, continuous learning, and decentralized infrastructure come together.