The field of neural architecture search and large language models is moving towards more efficient and effective methods. Researchers are exploring new approaches to improve the performance of these models, such as constrained iterative search and discrete representation learning. These innovations have the potential to significantly advance the field, enabling the development of more accurate and efficient models. Notable papers in this area include FACETS, which proposes a novel unified iterative NAS method, and Arch-LLM, which introduces a Vector Quantized Variational Autoencoder to learn a discrete latent space for neural architectures. Other papers, such as ToRL and Evolutionary Prompt Optimization, demonstrate the potential of tool-integrated reinforcement learning and evolutionary algorithms to improve the performance of large language models.
Advancements in Neural Architecture Search and Large Language Models
Sources
FACETS: Efficient Once-for-all Object Detection via Constrained Iterative Search
Arch-LLM: Taming LLMs for Neural Architecture Generation via Unsupervised Discrete Representation Learning
ToRL: Scaling Tool-Integrated RL
Evolutionary Prompt Optimization Discovers Emergent Multimodal Reasoning Strategies in Vision-Language Models
Cooper: A Library for Constrained Optimization in Deep Learning
ToolACE-R: Tool Learning with Adaptive Self-Refinement
LLM-Guided Evolution: An Autonomous Model Optimization for Object Detection
The Self-Learning Agent with a Progressive Neural Network Integrated Transformer