This paper presents a method to accelerate the grokking phenomenon, where a model's generalization improves with more training iterations after an initial overfitting stage. The authors propose a simple algorithmic modification to existing optimizers that filters out the fast-varying components of the gradients and amplifies the slow-varying components, thereby accelerating the grokking effect.
This article discusses the process of training a large language model (LLM) using reinforcement learning from human feedback (RLHF) and a new alternative method called Direct Preference Optimization (DPO). The article explains how these methods help align the LLM with human expectations and make it more efficient.
Delving into transformer networks
