3 papers indexed here
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
It is shown that self-conditioning corrects a train/inference mismatch by exposing the model to inference-like states that are otherwise rare during training, and cross-entropy drives embeddings towards a geometry whose decoding error rate closely matches the one-hot one.
PUMBA is introduced, a unified framework for trajectory-aware training that trains the denoiser on consecutive steps of policy-induced trajectories, passes information between steps, and optimizes them jointly by backpropagation through time.
It is shown that a step matches the training distribution only when the positions it writes are conditionally independent given the tokens already fixed, that no product of per-position distributions can match a dependent group, and that per-position distributions do not determine whether a group is dependent.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
