Masked Diffusion Language Models for Agentic RL World Models

Masked Diffusion Language Models offer a steerable alternative to autoregressive world models for reinforcement learning by decomposing dynamics into initial state, task context, and expected outcomes. This approach avoids the left-to-right bias of autoregressive models, allowing for better conditioning on global anchors like tool schemas.