Scaling · AI Beat

03 Aug 2026 · AI Beat Desk

Train on Your Best Guess

A new pretraining technique called Explorative Modeling adds a best-of-K selection step to the training loop — generate K candidates, keep the one closest to the target, backprop through only that one. The efficiency gains on image and video models are large and grow with scale, suggesting a genuine third axis alongside parameters and data. For autoregressive LLMs the gains are modest for now, but the underlying idea is worth watching.

17 Jul 2026 · AI Beat Desk

What Emerges at a Trillion

Ring-Zero scales pure reinforcement learning from verifiable task rewards — no human-labeled preference data — to one trillion parameters. Complex reasoning behaviors emerge spontaneously: self-verification, parallel reasoning, and something the authors call "context anxiety." The two-phase training dynamic (discovery then sharpening) appears to be a consistent pattern as these runs grow larger.