In the early days of adapter-based tuning, LoRA often felt like a charming hack—efficient, plausible, but with a nagging question: would performance always trail full fine-tuning? New research from Thinking Machines, led by John Schulman (co-founder of OpenAI and creator of the PPO algorithm), argues that the difference is not inevitable. Under the right regime,...
Tag: Fine-Tuning
LoRA vs. Fine-Tuning LLMs
LoRA (Low-Rank Adaptation) and fine-tuning are two methods to adapt large language models (LLMs) to specific tasks or domains. LLMs are pre-trained on massive amounts of general domain data, such as GPT-3, RoBERTa, and DeBERTa, and have shown impressive performance on various natural language processing (NLP) tasks. Why fine tune a LLM? Fine-tuning of LLMs...
