LLM News

Researchers introduce DiaBlo, a parameter-efficient fine-tuning method that updates only diagonal blocks of model weight matrices instead of full parameters. The approach matches full-model fine-tuning performance across reasoning, code generation, and safety tasks while maintaining comparable memory usage and training speed to LoRA.

Researchers have identified a critical gap in how large language models learn mathematics: they can solve problems but often don't understand the underlying concepts. A new reinforcement learning framework called CORE addresses this by using explicit concept definitions as training signals, rather than just reinforcing correct final answers.