Evaluating AI model performance is a necessity: it drives model selection, informs research, and allows us to reason about the frontier of machine intelligence. It’s also hard. Traditional approaches rely on human

💡BlockAssist is an AI Minecraft assistant that learns from your in-game actions. Today we are introducing BlockAssist, an AI assistant that learns from its user’s actions in Minecraft. The assistant appears in-game

Flexible, decentralised multi-agent RL environments Reinforcement Learning (RL) continues to prove its power in solving complex problems, from optimising systems to training intelligent agents. As we push the boundaries, especially in scenarios involving

This is an academic paper describing CheckFree, a novel recovery method for failures in distributed training that does not require checkpointing or redundant computation, enabling efficient training in the presence of frequent failures.

💡NoLoCo: training large models with no all-reduce This is an academic paper describing NoLoCo, a novel optimisation method for distributed training that replaces the global synchronisation step with a gossip method, enabling training