**Compute, robustness primitives & attention-residual architecture moves shape feasibility** [developing]
Key Questions
What is Test-Time Scaling?
Test-Time Scaling makes overtraining compute-optimal for LLMs. It improves efficiency in resource allocation during inference.
What are Gemma 4 model sizes?
Gemma 4 includes open 31B/26B/2B/4B MoE variants for edge multimodal use. They support 256K context for robust deployment.
What are Attention Residuals?
Attention Residuals join architectural moves with DataFlex and Moonwalk. They enhance compute efficiency and model robustness.
What is MegaTrain?
MegaTrain enables full-precision training of 100B+ parameter LLMs on a single GPU. It advances compute feasibility for large models.
What is DataFlex?
DataFlex is a unified framework for data-centric dynamic training of LLMs. It optimizes training with flexible data handling.
What is pmarca PD 22B?
pmarca references a 22B parameter model in PD context. It ties into compute primitives and self-distilled RLVR advancements.
What are RWKV v8 improvements?
RWKV v8 features self-distillation and continual learning. It shapes attention-residual architectures for better scalability.
What is the role of self-distilled RLVR?
Self-Distilled RLVR optimizes reward learning post-training. Surveys highlight its place in on-policy distillation for LLMs.
Test-Time Scaling makes overtraining compute-optimal joins Gemma 4 open 31B/26B/2B/4B MoE edge multimodal (256K ctx), Attention Residuals/DataFlex/Moonwalk/TAPS/RWKV v8/self-distil/continual/Self-Distilled RLVR; pmarca PD 22B; DL weights data structure.