Topic: inference-time computation

This talk surveys the components of modern diffusion language models, beginning with masked prediction and extending to iterative refinement, controlled generation and variable-length inference. It examines how parallel generation and repeated revision change inference-time computation. Examples from natural language, proteins and DNA illustrate both practical model designs and uses of fine-grained control in scientific sequence generation.

Sebastian Raschka reviews changes in language-model research, including reasoning-focused post-training, inference-time computation and reinforcement learning with verifiable rewards. The discussion connects these methods with self-consistency, model architectures, efficient attention and long contexts, while examining limits in continual learning and the reliability of agent workflows.