Citation :

Mohanakrishnan Hariharan, 2025. "Reinforcement Learning: Advanced Techniques for LLM Behavior Optimization" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 2: 84-101.

Abstract :

Reinforcement Learning (RL) has rapidly emerged as a powerful tool in many fields to provide intricate solutions for enhancing the decision-making process; applying RL to Large Language Models (LLMs) extended the ways of enhancing text generation. As for this paper, its primary concern is how RL, from the real-world application, deep reinforcement learning, policy gradient methods, and value-based methods, can go beyond conventional retraining for LLMs. The presented RL differs from fine-tuning in the ways that the latter refines the parameters of the model to enhance its accuracy on particular tasks with the help of labels. At the same time, RL modifies the actions of LLMs by using a reward signal on text generation. Fine-tuning can be more useful when working with certain sets which need to be adapted. At the same time, RL is always effective when it comes to continuous learning, which means that LLMs can learn during interaction and produce only those responses that are relevant to specific goals, which may be coherence, sentiment, or any specific task accuracy. By using RL, the complex high-dimensional state and action spaces of LLMs are managed using elements such as DQN and PPO that define the best policy and the expected reward regarding any action. Specifically, policy-gradient methods, REINFORCE, and Trust Region Policy Optimization (TRPO) are discussed for policy improvement, and value-department methods, Q-learning, and Advantage Actor-Critic (A2C) are considered for decision-making improvement throughout the text generation. RL-based models also trump fine-tuning in cases where adaptive learning occurs and the objective function changes with interactions happening in real-time. Furthermore, this paper also presents the combination of RL with unsupervised and supervised learning to show how the large textual corpora and task-specific data may improve LLMs. The issues of computational cost, the generation of reward functions, and maintaining the coherence of the generated text are elaborated as issues, and their corresponding solutions and possible future directions are described. Lastly, we find that RL is essential for making LLMs highly specialized, versatile, and efficient in numerous real-world tasks ranging from answering phone calls to blogging and creating lifelike virtual personalities.

References :

[1] Cameron R. Wolfe, Basics of Reinforcement Learning for LLMs, medium, online. https://towardsdatascience.com/basics-of-reinforcement-learning-for-llms-d74c5178cd2d

[2] What Is Reinforcement Learning? Working, Algorithms, and Uses, spiceworks, online. https://www.spiceworks.com/tech/artificial-intelligence/articles/what-is-reinforcement-learning/

[3] What Is LLM Optimization?, iguazio, online. https://www.iguazio.com/glossary/llm-optimization/

[4] Mousavi, S. S., Schukat, M., & Howley, E. (2018). Deep reinforcement learning: an overview. In Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016: Volume 2 (pp. 426-440). Springer International Publishing.

[5] Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.

[6] Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237-285.

[7] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.

[8] Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., ... & Wierstra, D. (2016). Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971*.

[9] Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.

[10] Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3-4), 229-256.

[11] Schulman, J., Levine, S., Moritz, P., Jordan, M., & Abbeel, P. (2015). Trust region policy optimization. In International Conference on Machine Learning (pp. 1889-1897). PMLR.

[12] Watkins, C. J. C. H., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3-4), 279-292.

[13] Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.

[14] Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., ... & Kavukcuoglu, K. (2016). Asynchronous methods for deep reinforcement learning. International Conference on Machine Learning (pp. 1928-1937). PMLR.

[15] Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., ... & Irving, G. (2019). Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593.

[16] Stiennon, N., Ziegler, D. M., Wu, J., Brown, T. B., Radford, A., Amodei, D., ... & Christiano, P. (2020). Learning to summarize with human feedback. arXiv preprint arXiv:2009.01325.

[17] Narasimhan, K. R., Kulkarni, T. D., & Barzilay, R. (2015). Language understanding for text-based games using deep reinforcement learning. arXiv preprint arXiv:1506.08941.

[18] Zhang, S., Bapna, A., Firat, O., Wang, Y., Chen, M. X., Chen, Z., ... & Wu, Y. (2018). Improving deep transformer with depth-scaled initialization and merged attention. arXiv preprint arXiv:1904.09483.

[19] Bahdanau, D., Hill, F., Leike, J., Hughes, E., Kohli, P., & Grefenstette, E. (2019). Learning to understand goal specifications by modelling reward. International Conference on Learning Representations.

[20] Advantage Actor-Critic (A2C) algorithm in Reinforcement Learning with Codes and Examples using OpenAI Gym, medium, online. https://medium.com/data-science-in-your-pocket/advantage-actor-critic-a2c-algorithm-in-reinforcement-learning-with-codes-and-examples-using-e810273c0c9e

Keywords :

Behavior Optimization, Deep Reinforcement Learning, Large Language Models, Policy Gradient Methods, Reinforcement Learning, Supervised Learning, Text Generation, Unsupervised Learning, Value-Based Methods.