BeamGPT: A new paradigm for attention

According to LessWrong, an independent researcher has found a hybrid attention mechanism that reportedly outperforms standard transformer attention. The key innovation: a new operator that's linear in sequence length instead of quadratic. In early tests, models learn to allocate roughly forty-five percent of their computation to traditional attention and fifty-five percent to this new field operator, achieving about two point three times the computational savings at long contexts. Here's the twist: the researcher is deliberately keeping the mathematical details under wraps, citing the potential impact on pretraining efficiency. He's currently unaffiliated and seeking the right lab to develop it further.

Listen to this story

Hear this and more stories in a personalized audio briefing.

Open The Chonkerton