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cs.LG2026
Trapped by Their Own Rollouts: Understanding Aggregation--Rollout Feedback in Federated On-Policy Distillation
Jinqian Chen, Jihua Zhu, Chang Liu
On-policy distillation (OPD) is a promising approach to language-model adaptation, aligning teacher supervision with the student's own generated trajectories. When adaptation promp…
cs.LG2026
Beyond Factor Aggregation: Gauge-Aware Low-Rank Server Representations for Federated LoRA
Jinqian Chen, Chang Liu, Jihua Zhu
Federated LoRA enables parameter-efficient adaptation of large language models under decentralized data and limited client resources.However, directly averaging LoRA factors is rep…
cs.LG2026
Learngene Search Across Multiple Datasets for Building Variable-Sized Models
Boyu Shi, Junbo Zhou, Chang Liu +3
Deep learning methods are widely used under diverse resource constraints, resulting in models of varying sizes, such as the Vision Transformer (ViT) series. Deploying these models…