The field of artificial intelligence is witnessing a significant shift towards integrating language models into reinforcement learning and algorithm design. Researchers are exploring ways to leverage large language models (LLMs) to improve the performance of reinforcement learning agents and automate algorithm design. This direction is driven by the potential of LLMs to generate high-quality instructions, optimize prompts, and adapt to specific tasks. A major focus area is the development of novel frameworks and techniques that can effectively utilize LLMs in these applications. Noteworthy papers in this area include:

• elsciRL, which introduces an open-source library for integrating language solutions into reinforcement learning problems.
• Grammar-Guided Evolutionary Search, which proposes an evolutionary search approach to automated discrete prompt optimisation.
• Fine-tuning Large Language Model, which explores fine-tuning of LLMs for algorithm design and demonstrates significant performance improvements.
• Step-wise Policy for Rare-tool Knowledge, which teaches LLMs to explore diverse tool usage patterns.
• Auto-Formulating Dynamic Programming Problems, which introduces a specialized model for automating the formulation of dynamic programming problems.