This tutorial provides a comprehensive survey of methods for fine-tuning diffusion models to optimize downstream reward functions. Diffusion models are powerful tools for generative modeling, but they often need to be customized for specific downstream objectives, such as maximizing translation efficiency in RNA or stability in proteins. The tutorial explores various reinforcement learning (RL) algorithms, including PPO, differentiable optimization, reward-weighted MLE, value-weighted sampling, and path consistency learning, tailored for fine-tuning diffusion models. It discusses the strengths and limitations of different RL-based fine-tuning algorithms, their formal objectives, and connections with related topics like classifier guidance and flow-based diffusion models. The tutorial aims to help readers understand the fundamentals of RL-based fine-tuning and select the most suitable algorithms for their specific applications. The code for this tutorial is available at <https://github.com/masa-ue/RLfinetuning_Diffusion_Bioseq>.This tutorial provides a comprehensive survey of methods for fine-tuning diffusion models to optimize downstream reward functions. Diffusion models are powerful tools for generative modeling, but they often need to be customized for specific downstream objectives, such as maximizing translation efficiency in RNA or stability in proteins. The tutorial explores various reinforcement learning (RL) algorithms, including PPO, differentiable optimization, reward-weighted MLE, value-weighted sampling, and path consistency learning, tailored for fine-tuning diffusion models. It discusses the strengths and limitations of different RL-based fine-tuning algorithms, their formal objectives, and connections with related topics like classifier guidance and flow-based diffusion models. The tutorial aims to help readers understand the fundamentals of RL-based fine-tuning and select the most suitable algorithms for their specific applications. The code for this tutorial is available at <https://github.com/masa-ue/RLfinetuning_Diffusion_Bioseq>.