Align Anything is a framework for training multimodal models with human feedback across any combination of input and output modalities.
The project addresses the challenge of aligning large models that process diverse modalities—images, video, audio, and text—with human intentions and values. It provides a modular architecture that enables fine-tuning of any-to-any models using multiple alignment methods including supervised fine-tuning, direct preference optimization, reinforcement learning from human feedback, and rule-based reinforcement learning approaches. The framework includes a multimodal command-line interface for working with different modalities and supports specialized training techniques such as reasoning-focused training based on chain-of-thought datasets.
Teams should adopt this tool if they are working on multimodal model alignment and need flexibility to experiment with different training approaches and model architectures. The highly modular design allows customization for specific tasks without requiring extensive modifications to the core codebase. The project is particularly suited for researchers and practitioners building vision-language models, video understanding systems, or other cross-modal applications who want to incorporate human feedback into their training pipelines. The framework supports multiple state-of-the-art models and includes dedicated evaluation tools through its integrated eval-anything project.
Development activity shows active expansion of model support with recent integration of new model families. The project maintains a public roadmap indicating planned work on additional inference engines, advanced vision-language-agent algorithms, and enhanced reinforcement learning training features. The codebase includes specialized sub-projects such as InterMT for multi-turn interleaved preference alignment and eval-anything for large-scale model evaluation, suggesting a structured approach to extending capabilities. Documentation is provided through a dedicated read-the-docs site alongside quick-start guides in the repository.
