Radar ·
GOAT adds trainable priors to attention
Radar: a paper that reads standard attention as Entropic Optimal Transport with an implicit uniform prior and proposes GOAT, with a continuous trainable prior that stays compatible with optimized kernels such as FlashAttention.
Radar records a paper that interprets standard attention as an Entropic Optimal Transport problem with an implicit uniform prior. The work presents GOAT, which uses a continuous trainable prior and states that it keeps compatibility with optimized kernels, such as FlashAttention. According to the available summary, engineers can evaluate this way of adding trainable priors while preserving that compatibility.
The relevance lies in model architecture, and Rota Nacional does not implement GOAT or guarantee the gains described. To verify, locate the original paper by the title and date indicated, read the experimental results and reproduce them in your own environment. If you use an AI assistant to study the material, do not paste internal data, proprietary code or texts containing personal data; the platform detects CPF, CNPJ, e-mail, phone numbers and names before any model runs and applies placeholders, removal or blocking according to the organization's policy.
Original source: GOAT adiciona priors treináveis à attention