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cs.IR2026
Optimizing Effective Training Time for Large-Scale Recommendation Systems
Mingming Ding, Ruilin Chen, Yuzhen Huang +29
Lifecycle overhead silently consumes accelerator capacity across large-scale recommendation training fleets. Our largest recommendation workloads process tens of billions train- in…
cs.LG2026
Adaptive Block Diffusion: Resolving Training-Inference Mismatch in Diffusion Language Models
Gagan Jain
Diffusion Language Models (DLMs) are typically trained under fixed context structures, restricting denoising to predetermined token subsets. This creates a mismatch between trainin…