7 citations · 18 across the 6 of their papers we have counts for
4 papers · 1 filter
Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning
Ruizhe Shi, Zaiwen Yang, Qiubo Wang +2
We study sparse and continual reinforcement learning fine-tuning of language models through attention-head ranking and partitioning. For sparse learning, we rank attention heads by…
How to Evaluate Reward Models for RLHF
Evan Frick, Tianle Li, Connor Chen +6
We introduce a new benchmark for reward models that quantifies their ability to produce strong language models through RLHF (Reinforcement Learning from Human Feedback). The gold-s…
From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline
Tianle Li, Wei-Lin Chiang, Evan Frick +5
The rapid evolution of Large Language Models (LLMs) has outpaced the development of model evaluation, highlighting the need for continuous curation of new, challenging benchmarks.…
S-LoRA: Serving Thousands of Concurrent LoRA Adapters
Ying Sheng, Shiyi Cao, Dacheng Li +9
The "pretrain-then-finetune" paradigm is commonly adopted in the deployment of large language models. Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning method, is often…