Do We Need Adam? Surprisingly Strong and Sparse Reinforcement Learning with SGD in LLMs

The article reveals that reinforcement learning (RL) in large language models (LLMs) can achieve similar or better performance using stochastic gradient descent (SGD), which is significantly more memory-efficient and updates fewer parameters compared to AdamW optimizer. This finding suggests RL may be more parameter-efficient than previously thought, offering content creators a potentially less resource-intensive method for training LLMs with RL techniques.

Read the full article at arXiv cs.LG (ML)

---

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.