Chenxiao Yang

Transformers require a surprisingly high number of examples for effective chain-of-thought learning, challenging assumptions about their efficiency.

Recursive models can exponentially shrink the context window needed for long-horizon reasoning, blowing past the limitations of even the biggest LLMs.