Transformer Architecture

For years, the AI community operated under a seemingly unshakeable assumption: the remarkable capabilities of large language models—from in-context learning to instruction following—inherently depend on autoregressive architectures. GPT, LLaMA, Claude, and virtually every dominant LLM shares the same fundamental design: predict the next token given all previous tokens. But what if this assumption was wrong? In February 2025, a paper from researchers at Renmin University of China challenged this paradigm with striking empirical evidence. LLaDA (Large Language Diffusion with mAsking), an 8B-parameter model trained entirely from scratch using diffusion processes, achieved performance competitive with LLaMA3 8B across diverse benchmarks. More remarkably, it solved problems that have plagued autoregressive models for years—the reversal curse being the most prominent. This isn’t merely an architectural curiosity; it’s a fundamental re-examination of how language models can learn and reason. ...

In April 2025, Meta’s Llama 4 Scout achieved something previously thought impossible: processing 10 million tokens in a single context window. To put this in perspective, that’s roughly 20 novels, 40 hours of video, or an entire mid-sized codebase—all in one prompt. The secret behind this breakthrough isn’t a revolutionary new model architecture or exotic hardware. It’s a clever distributed computing technique called Ring Attention that fundamentally rethinks how we compute attention across multiple GPUs. ...