Progress and Analysis of Optimization for Large Language Models Based on Reinforcement Learning
TL;DR
The boundaries of how reinforcement learning optimizes the reasoning capabilities of large language models under different resource and scale constraints are reviewed, as well as the adaptability of reinforcement learning's optimization of large language models in different scenarios are analyzed.
Abstract
Large language models (LLMs) are one of the current research focuses in society and have extensive applications in various fields. However, when facing complex tasks such as mathematical reasoning at present, it will exhibit problems such as weak generalization ability. Reinforcement Learning (RL) can effectively optimize these issues through reward-guided strategies, thus becoming the core technical path for enhancing the reasoning capabilities of large language models. This article systematically reviews and analyzes the boundaries of how reinforcement learning optimizes the reasoning capabilities of large language models under different resource and scale constraints, as well as the adaptability of reinforcement learning's optimization of large language models in different scenarios. The analysis shows that reinforcement lea rning algorithms are beneficial for aligning model reasoning and improving the reasoning chain of the model; based on reinforcement learning, strategies such as data minimization training and small model optimization can enhance the resource utilization ef ficiency of the model during training; however, the improvement of reinforcement learning algorithms on large language models still has problems such as difficulties in expanding the reasoning boundaries.
