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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 \\emph{loss profiling}, which ranks candidate orders by the early-stage loss of a single short run. To handle the factorial candidate space, we wrap loss profiling in a hierarchical global -- local search over block- and within-block-level orderings. On six order-sensitive tasks, the method discovers effective orders up to $L=13$ from random initialization and up to $L=40$ from structured initialization, lifting success rates from about 10\\% to near 100\\%. On integer multiplication, it rediscover","title":"Discovering Learning-Friendly Generation Orders for Sequential Computation","url":"https://arxiv.org/abs/2506.23875","vendor":"arxiv_cs_ai"},"summary":"arXiv:2506.23875v4 Announce Type: replace-cross \nAbstract: 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 \\emph{loss profiling}, which ranks candidate orders by the early-stage loss of a single short run. To handle the factorial candidate space, we wrap loss profiling in a hierarchical global -- local search over block- and within-block-level orderings. On six order-sensitive tasks, the method discovers effective orders up to $L=13$ from random initialization and up to $L=40$ from structured initialization, lifting success rates from about 10\\% to near 100\\%. 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