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On imbalanced data, a fixed weighting between hard and soft losses becomes brittle the learning process. Recent studies try to reweight these components in long-tailed settings. However, most of these meth- ods do not adapt weights at the sample-wise level and do not take into account the students behavior during training. To address this, we pro- pose BiKD - a bilevel framework that dynamically balances hard and soft losses for each sample. We employ a weight generation network that produces adaptive per-sample weights, guided by a small balanced vali- dation set. The student is now trained with an unconstrained combina- tion of weighted hard and soft losses, allowing the student to relax both terms. We further propose a multi-step SGD strategy to optimize the weight model more accurately and efficientl","title":"Balancing Knowledge Distillation for Imbalance Learning with Bilevel Optimization","url":"https://arxiv.org/abs/2605.17839","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.17839v1 Announce Type: cross \nAbstract: Knowledge distillation transfers knowledge from a high capacity teacher to a compact student using a mixture of hard and soft losses. On imbalanced data, a fixed weighting between hard and soft losses becomes brittle the learning process. Recent studies try to reweight these components in long-tailed settings. However, most of these meth- ods do not adapt weights at the sample-wise level and do not take into account the students behavior during training. To address this, we pro- pose BiKD - a bilevel framework that dynamically balances hard and soft losses for each sample. We employ a weight generation network that produces adaptive per-sample weights, guided by a small balanced vali- dation set. The student is now trained with an unconstrained combina- tion of weighted hard and soft losses, allowing the student to relax both terms. 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