aiqbench analysis
DisaggKD addresses critical memory bottlenecks in knowledge distillation by disaggregating teacher and student models across separate nodes, enabling training of larger model pairs that would otherwise exceed GPU budgets. The approach maintains exact arithmetic fidelity for the full-vocabulary loss while improving efficiency through fused loss functions and optimized data streaming between distributed components. This method represents a significant advancement in scalable machine learning infrastructure for large language model fine-tuning.
DisaggKD: Efficient Knowledge Distillation through Teacher-Student Disaggregation and a Fused Loss
arXiv:2610.17644v1 Announce Type: cross Abstract: Knowledge distillation (KD) frameworks place teacher and student on the same GPUs, so per-GPU memory limits the teachers that fit. DisaggKD separates them across nodes: teacher replicas stream hidden states to the student, a KD loss over row windows and vocabulary shards equals the full-vocabulary loss in exact arithmetic without forming the full-vocabulary logits, and a prefetch ring paces producer and trainer. Closed-form memory and traffic models locate the memory limits of each layout. On the Qwen3.5-35B-A3B to 9B pair, the disaggregated layout matches the LM loss of the colocated run with the same loss and is faster than that run; our fused loss also lets a colocated run train on as few nodes at faster speed, while the split is memory efficient when the student spans several nodes. On the larger pairs, colocated runs with a fused loss still run OOM where the split trains, because teacher weights and student states fill the budget.
Abstract:Knowledge distillation (KD) frameworks place teacher and student on the same GPUs, so per-GPU memory limits the teachers that fit. DisaggKD separates them across nodes: teacher replicas stream hidden states to the student, a KD loss over row windows and vocabulary shards equals the full-vocabulary loss in exact arithmetic without forming the full-vocabulary logits, and a prefetch ring paces producer and trainer. Closed-form memory and traffic models locate the memory limits of each layout. On the Qwen3.5-35B-A3B to 9B pair, the disaggregated layout matches the LM loss of the colocated run with the same loss and is faster than that run; our fused loss also lets a colocated run train on as few nodes at faster speed, while the split is memory efficient when the student spans several nodes. On the larger pairs, colocated runs with a fused loss still run OOM where the split trains, because teacher weights and student states fill the budget.
Machine Learning (cs.LG); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
arXiv:2610.17644 [cs.LG]
(or arXiv:2610.17644v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.17644 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 259 words
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