Discovering Learning-Friendly Generation Orders for Sequential Computation

arXiv:2506.23875v4 Announce Type: replace-cross Abstract: Sequential computation via autoregressive generation can make difficult tasks learnable, but the generation order of intermediate states strongly affects whether training succeeds. We address the problem of discovering a learning-friendly target order automatically, rather than relying on task-specific design. Our key observation is that learning-friendly orders cause a faster loss drop in the early stage of training. We exploit this by \