5 papers
Right Makes Might: Aligning Verified Hidden States Empowers RL Reasoning
Ziyue Wang, Aomufei Yuan, Yongfu Zhu +10
Reinforcement Learning from Verifiable Rewards (RLVR) has become the dominant approach for improving mathematical reasoning in large language models, yet current methods reduce eac…
Beyond Parameter Arithmetic: Sparse Complementary Fusion for Distribution-Aware Model Merging
Weihong Lin, Lin Sun, Qilong Shi +6
Model merging has emerged as a promising paradigm for composing the capabilities of large language models by directly operating in weight space, enabling the integration of special…
KVReviver: Reversible KV Cache Compression with Sketch-Based Token Reconstruction
Aomufei Yuan, Zhiming Wang, Ruijie Miao +8
As the context length of current large language models (LLMs) rapidly increases, the memory demand for the Key-Value (KV) cache is becoming a bottleneck for LLM deployment and batc…
KeepKV: Achieving Periodic Lossless KV Cache Compression for Efficient LLM Inference
Yuxuan Tian, Zihan Wang, Yebo Peng +6
Efficient inference of large language models (LLMs) is hindered by an ever-growing key-value (KV) cache, making KV cache compression a critical research direction. Traditional meth…
FairKV: Balancing Per-Head KV Cache for Fast Multi-GPU Inference
Bingzhe Zhao, Ke Cheng, Aomufei Yuan +5
KV cache techniques in Transformer models aim to reduce redundant computations at the expense of substantially increased memory usage, making KV cache compression an important and…