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Co-Adaptive Multi-Task LoRA: Transfer-Aware, Label-Free Control of Domain Participation
Wei Zhang, Lin Tang, Ming Zhao +1
Fine-tuning a single low-rank adapter on many domains at once is multi-task learning: the domains must be co-learned, and how they share the adapter decides whether they help or hu…
ReNIO: Reweighting Negative Trajectory Importance for LLM On-Policy Distillation
Chen Lin, Kedi Chen, Wei Zhang
On-policy distillation (OPD) improves LLM reasoning by training a student model on its own generated outputs, but standard OPD treats all student-generated outputs (SGOs) equally r…
UniRank: Unified Rank Allocation for Low-Rank LLM Compression
Chao Han, Haozhe Hu, Yongjie Du +5
Low-rank decomposition is a promising compression paradigm for large language models (LLMs), yet its effectiveness hinges on rank budget allocation across weight matrices: uniform…
Predicting Mergeability of Parameter-Efficient Fine-Tuning Updates
Lin Tang, Wei Zhang, Jing Li +3
Low-rank adaptation (LoRA) makes it cheap to train many domain- and task-specific language model adapters, but whether two adapters can be merged is usually discovered only after b…
LoopCoder-v2: Only Loop Once for Efficient Test-Time Computation Scaling
Jian Yang, Shawn Guo, Wei Zhang +16
Looped Transformers scale latent computation by repeatedly applying shared blocks, but sequential looping increases latency and KV-cache memory with the loop count. Parallel loop T…