Paper

$\phi$-Balancing for Mixture-of-Experts Training

Unreadunread

AI summary

arXiv:2605. 15403v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models rely on balanced expert utilization to fully realize their scalability.

Reading status

Priority

About this paper

Authors: Lizhang Chen, Jonathan Li, Qi Wang, Runlong Liao, Shuozhe Li, Chen Liang, Ni Lao, Qiang Liu
Released: 2026-05-18 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because $\phi$-Balancing for Mixture-of-Experts Training overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)", clean result "A pretraining-data-poisoned Qwen3-4B backdoor only fires on the exact trigger tokens — paraphrases don't activate it, and base-model similarity to the trigger doesn't predict which inputs fire (MODERATE confidence)". Matching terms: rect, line, rate, full, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2605.15403

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to $\phi$-Balancing for Mixture-of-Experts Training. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.