Tevatron Meets Megatron: Expert-Parallel LLM Reranker Training on an Academic Budget
Modern reranking recipes---billion-scale cross-encoders, mixture-of-experts (MoE) backbones, and distillation against strong teachers---have outpaced the training infrastructure available to most academic groups. Existing Tevatron reranker training relies on the Hugging Face Trainer with DeepSpeed or PyTorch FSDP1, but these backends lack efficient support for large-scale MoE training. We present Tevatron 3.0, which integrates a Megatron-Core training backend into Tevatron while preserving its data pipeline, evaluation workflow, and Hugging Face-compatible checkpoints. We benchmark existing distributed training configurations against the new backend, showing that Megatron matches FSDP rer...
| Authors | Zhichao Xu, Xueguang Ma, Shengyao Zhuang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 2 August 2026 |
| Last updated at source | 2 August 2026 |
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