RL4VLM

An open-source project that fine-tunes large vision-language models via reinforcement learning to act as decision-making agents.

CommonProductProgrammingReinforcement LearningVision-Language Models

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RL4VLM is an open-source project aimed at fine-tuning large vision-language models via reinforcement learning, enabling them to function as intelligent agents capable of making decisions. Developed collaboratively by researchers including Yuexiang Zhai, Hao Bai, Zipeng Lin, Jiayi Pan, Shengbang Tong, Alane Suhr, Saining Xie, Yann LeCun, Yi Ma, and Sergey Levine, it is based on the LLaVA model and employs the PPO algorithm for reinforcement learning fine-tuning. RL4VLM provides a comprehensive codebase structure, installation guidelines, licensing information, and instructions on how to cite the research.

Provides a modified LLaVA model.
Original GymCards environment.
RL4VLM codebase for GymCards and ALFWorld environments.
Detailed training procedures
including preparing SFT checkpoints and running RL using SFT checkpoints.
Offers two different conda environments to accommodate the diverse package requirements of GymCards and ALFWorld.
Provides detailed guidelines and template scripts for running algorithms.
Emphasizes the importance of using specific checkpoints as starting points and offers flexibility in using different initial models.

Target audience primarily researchers and developers in the field of machine learning and artificial intelligence who need to leverage vision-language models for decision-making and reinforcement learning research.

Researchers utilize RL4VLM to fine-tune models
enhancing decision-making capabilities in natural language processing tasks.
Developers leverage the project's codebase and environments to train customized vision-language models.
Educational institutions employ RL4VLM as a teaching case study
demonstrating to students how reinforcement learning can elevate model performance.

First
visit the RL4VLM GitHub page to access project information and the codebase.
Prepare the necessary SFT checkpoints according to the provided installation guidelines.
Download and configure the required conda environment for GymCards or ALFWorld.
Run the fine-tuning process for LLaVA following the guidelines

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