16 citations · 17 across the 16 of their papers we have counts for
5 papers · 1 filter
HiLoRe: What to Store, Compress, or Recompute for Efficient GRPO Training
Xinrui Chen, Mengyang Li, Ou Wu +1
Group-relative policy optimization (GRPO) makes learner-side activations a major memory-computation bottleneck: gradient checkpointing reduces activation memory through recomputati…
CompKV: Compensation-Aware KV Selection for Long-Context LLM Inference
Zhen Huang, Ruizhe Yao, Danyi Liu +8
Despite their strong performance, large language models (LLMs) are bottlenecked by KV cache memory traffic during long-context inference. Sparse attention is widely used to acceler…
Token Utility Is Selection-Conditioned: Coupled Selection of Prompt Context and Response Supervision for Instruction Tuning
Can Wu, Xinrui Chen, Ou Wu +1
Token selection for instruction tuning is coupled: supervision utility depends on retained context, and vice versa. Existing methods largely optimize prompt compression or response…
Two to Tango: Coupled Task-Reference Selection for Safe LLM Fine-tuning
Xinrui Chen, Jianhao Zhang, Ou Wu +1
Fine-tuning safety aligned large language models (LLMs) on downstream data improves adaptation but may erode learned safety behavior. Existing methods use fixed safety examples, gl…
One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning
Xinrui Chen, Liu Yang, Ou Wu
In Large Language Model (LLM) fine-tuning, parameter and data selection are common strategies for reducing fine-tuning cost, yet they are typically driven by separate scoring mecha…