5 papers
Sample-Adaptive Zeroth-Order Post-Training
Haiyuan Wan, Yanyi Li, Weicheng Lin +1
Zeroth-order (ZO) optimization offers a memory-efficient alternative to backpropagation for fine-tuning large language models. However, existing ZO methods typically sample trainin…
Beyond Imitation: Filtering On-Policy Distillation by Reasoning Progress
Chen Yang, Haiyuan Wan, Rengrong Xiong +2
On-policy distillation (OPD) has emerged as an effective framework for post-training language models by pairing student-generated trajectories with dense token-level supervision fr…
SDFLoRA: Selective Decoupled Federated LoRA for Privacy-preserving Fine-tuning with Heterogeneous Clients
Zhikang Shen, Jianrong Lu, Haiyuan Wan +1
Federated learning (FL) for large language models (LLMs) has attracted increasing attention as a privacy-preserving approach for adapting models over distributed data, where parame…
RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging
Bowen Wang, Haiyuan Wan, Liwen Shi +10
We unveil that internal representations in large language models (LLMs) serve as reliable proxies of learned knowledge, and propose RECALL, a novel representation-aware model mergi…
Spotlight Attention: Towards Efficient LLM Generation via Non-linear Hashing-based KV Cache Retrieval
Wenhao Li, Yuxin Zhang, Gen Luo +4
Reducing the key-value (KV) cache burden in Large Language Models (LLMs) significantly accelerates inference. Dynamically selecting critical KV caches during decoding helps maintai…