The field of large language models (LLMs) is moving towards more efficient and scalable optimization methods. Researchers are exploring alternative approaches to traditional gradient-based optimizers, such as evolutionary algorithms and stochastic differential equations, to reduce computational costs and improve training times. Additionally, there is a growing focus on developing asynchronous and decentralized training frameworks to accelerate reinforcement learning (RL) post-training and improve model performance. Noteworthy papers in this area include EA4LLM, which proposes a gradient-free approach to LLM optimization, and Laminar, a scalable asynchronous RL post-training framework that achieves significant training throughput speedups. QeRL is also notable for its quantization-enhanced RL framework that enables efficient training of large LLMs on a single GPU. Overall, these advancements have the potential to make LLM training more accessible and efficient, enabling wider adoption and further innovation in the field.
Optimization and Scaling of Large Language Models
Sources
EA4LLM: A Gradient-Free Approach to Large Language Model Optimization via Evolutionary Algorithms
A Stochastic Differential Equation Framework for Multi-Objective LLM Interactions: Dynamical Systems Analysis with Code Generation Applications
Part II: ROLL Flash -- Accelerating RLVR and Agentic Training with Asynchrony
QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
Boundary-Guided Policy Optimization for Memory-efficient RL of Diffusion Large Language Models
Laminar: A Scalable Asynchronous RL Post-Training Framework
The Art of Scaling Reinforcement Learning Compute for LLMs
Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling