Ternary training without master weights
Two-timescale threshold flips (TTF) train ternary {-1, 0, 1} language models with no full-precision copy of the weights. A weight moves one level when low-rank, accumulated gradient evidence crosses a threshold, so the state is about a byte per weight instead of twelve. Ahead of master-weight training at 110M, level at 1.3B and 2.9B; a 23B model trains on one 16 GB GPU.