MAD-OPD recasts on-policy distillation teachers as a debating collective to supply better supervision, lifting agentic and code performance over single-teacher OPD across multiple model sizes.
GKD: A general knowledge distillation framework for large-scale pre-trained language model
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The paper introduces an HPC-aware teacher-student partitioning strategy for knowledge distillation that combines vertical and horizontal splits and reports up to 67% higher throughput than the symmetric TRL baseline.
citing papers explorer
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MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate
MAD-OPD recasts on-policy distillation teachers as a debating collective to supply better supervision, lifting agentic and code performance over single-teacher OPD across multiple model sizes.
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Optimizing Teacher-Student Partitioning for Scalable Knowledge Distillation on HPC Systems
The paper introduces an HPC-aware teacher-student partitioning strategy for knowledge distillation that combines vertical and horizontal splits and reports up to 67% higher throughput than the symmetric TRL baseline.