Title: MoECondenser: Finetuning MoE LLMs with Condenser Experts
Primary area: foundation or frontier models, including LLMs
Keywords: Mixture-of-Expert, large language model fine-tuning

Abstract: Despite MoE models leading in benchmarks, supervised fine-tuning (SFT) for the MoE architecture remains difficult because its router layers are fragile. Methods such as DenseMixer and ESFT mitigate collapse with dense mixing or auxiliary load-balancing losses, but these introduce noisy gradients that often degrade performance. In preliminary experiments, we systematically removed experts and observed that while certain “super experts” are activated far more frequently, discarding less used experts still leads to notable performance degradation. This suggests that even rarely activated experts encode non-trivial knowledge useful for downstream tasks. Motivated by this, we propose a new auxiliary loss free MoE SFT framework that combines router biases with shared condenser experts. Instead of enforcing balanced activation across all experts, our method leverages bias updates to encourage imbalanced and sparse routing, allowing rarely used experts to become inactive while designating two existing experts as shared condensers that aggregate knowledge from the inactive set without increasing the per-token compute budget. Router stability is maintained entirely through bias updates that regulate token-level and expert-level activation, eliminating the need for auxiliary losses. Experiments on large-scale MoE models demonstrate that our approach outperforms state-of-the-art SFT baselines such as DenseMixer and ESFT, achieving 4%+ gain on both mathematical reasoning and commonsenseQA benchmarks. Pruning and interexpert correlation analyses confirm that our condenser experts aggregate knowledge from the long-tail experts, preserving performance under sparse routing.

TL;DR: post-training/ finetuning method for mixture-of-expert large language models