RL4VLM

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Introduce：

RL4VLM is an open source project that aims to fine-tune large visual-language models through reinforcement learning to become intelligent agents capable of making decisions. The project was developed by Yuexiang Zhai, Hao Bai, Zipeng Lin, Jiayi Pan, Shengbang Tong, Alane Suhr, Saining Xie, Yann LeCun, Yi Ma, Co-developed by Sergey Levine and other researchers. It is based on LLaVA model and adopts PPO algorithm for reinforcement learning fine-tuning. The RL4VLM project provides a detailed codebase structure, getting started guide, license information, and instructions on how to cite the research.

Stakeholders:
The target audience is primarily researchers and developers in the field of machine learning and artificial intelligence who need to utilize visual-language models for decision making and reinforcement learning research.
Usage Scenario Examples:

• The researchers used RL4VLM to fine-tune the model to improve decision making in natural language processing tasks.
• Developers use the codebase and environment provided by the project to train custom visual-language models.
• Educational institutions use RL4VLM as a teaching case to show students how to improve model performance through reinforcement learning.

The features of the tool:

• A modified version of the LLaVA model is provided.
• Original GymCards environment.
• RL4VLM code base for GymCards and ALFWorld environments.
• A detailed training process, including preparing SFT checkpoints and running RL using SFT checkpoints.
• Two different conda environments are provided to accommodate the different package needs of GymCards and ALFWorld.
• Detailed guidelines and template scripts for running the algorithm are provided.
• The importance of using specific checkpoints as a starting point is emphasized and flexibility is provided to use different initial models.

Steps for Use:

• First, visit RL4VLM’s GitHub page for project information and codebase.
• Prepare the required SFT checkpoints according to the introductory guide provided.
• Download and set up the desired conda environment to accommodate GymCards or ALFWorld.
• Follow the guide to run LLaVA’s fine-tuning process, setting the necessary parameters such as data path and output directory.
• Use the provided template script to run the RL algorithm and configure the number of Gpus and related parameters.
• Adjust the parameters in the configuration file, such as num_lirocesses, according to the needs of the experiment.
• Run the RL algorithm and monitor the training process and model performance.
• Reference the RL4VLM project correctly according to the reference guidelines provided by the project.

Tool’s Tabs: Reinforcement learning, visual-language model