Efficient Neural Network Optimisation via Sequential Policy Gradient Modelling.

Sequential Policy Gradient (SPG) modelling, inspired by large language model architectures, presents a computationally efficient method for optimising machine learning models. Experiments across five datasets, including ImageNet and GLUE, demonstrate performance gains when models are retrained using SPG, exceeding standard transfer learning techniques with reduced computational cost.

The optimisation of artificial neural networks, a computationally intensive process, frequently limits the practical application of machine learning despite advances in algorithmic efficiency. Researchers continually seek methods to reduce the time and resources required to tune hyperparameters, the settings that govern a model’s learning process. A new approach, Sequential Policy Gradient (SPG) modelling, offers a potentially more efficient pathway to this optimisation, leveraging principles from large language model architectures to generate complete optimisation trajectories in a single computational pass. Zheng Li, Jerry Cheng, and Huanying Gu, all from the Department of Computer Science at the New York Institute of Technology, detail this method in their article, “Sequential Policy Gradient for Adaptive Hyperparameter Optimization”, demonstrating performance gains across diverse datasets including ImageNet, COCO, GLUE, SQuAD, and SUPERB, while maintaining reduced computational demands.

Automated machine learning necessitates efficient hyperparameter optimisation, and researchers continually seek methods that reduce computational burden without compromising performance. Sequential Policy Gradient (SPG) modelling presents a novel approach to trajectory generation, directly addressing the limitations of conventional reinforcement learning techniques in this domain. Inspired by the multi-token prediction architecture of DeepSeek-V3, a large language model, SPG empowers a base model to generate complete state-action trajectories within a single forward pass, achieving efficiency gains through the strategic addition of temporary modules.

Conventional policy gradient methods require iterative steps, repeatedly evaluating actions and updating the policy, which demands substantial computational resources and time. Policy gradients are a type of reinforcement learning algorithm that attempts to find a set of parameters for a policy, which dictates the behaviour of an agent, by estimating the gradient of the expected reward with respect to those parameters. SPG fundamentally alters this process by predicting a sequence of actions based on the current state, streamlining optimisation and accelerating model development. Experiments demonstrate that models consistently benefit from retraining using SPG on their original datasets, surpassing the performance achieved through standard transfer fine-tuning techniques, where a model pre-trained on a large dataset is adapted to a specific task.

Evaluations across five diverse datasets – ImageNet and COCO for computer vision, GLUE and SQuAD for natural language understanding, and SUPERB for audio processing – confirm the broad applicability of SPG. ImageNet and COCO are benchmark datasets used for training and evaluating image recognition and object detection models respectively. GLUE (General Language Understanding Evaluation) and SQuAD (Stanford Question Answering Dataset) are standard benchmarks for evaluating natural language understanding capabilities. SUPERB (Spoken Understanding Benchmarking) is a benchmark for evaluating speech recognition and understanding systems. The method consistently improves performance across a range of established models, delivering gains of up to 2.1% relative to baseline approaches and showcasing its versatility. This improvement is achieved with significantly reduced computational costs, making SPG particularly valuable for large-scale models and complex tasks where resources are often limited.

Researchers report significant reductions in training time and resource consumption when employing SPG, highlighting its practical benefits. The availability of fully reproducible code and pre-trained models, hosted on Hugging Face, further promotes accessibility and encourages wider adoption of this promising new technique. This commitment to open science facilitates verification of the results and fosters collaborative exploration of SPG’s potential in various machine learning applications.

SPG offers a streamlined and computationally efficient approach to optimisation, building upon previous research into neural architecture search, a process that automates the design of neural networks. By enabling the development of more efficient and powerful artificial intelligence systems, SPG contributes to advancements across diverse domains.

The core innovation lies in the incorporation of temporary modules into the base model, which facilitate the generation of the complete trajectory. These modules effectively predict a sequence of actions based on the current state, contrasting with conventional methods where the model repeatedly evaluates actions and updates its policy.

SPG reframes the process of generating optimisation trajectories, moving away from iterative methods towards a single-pass approach. The incorporation of temporary modules into the base model facilitates the generation of the complete trajectory, effectively predicting a sequence of actions based on the current state.