MIRL Roadmaps

[DDVD233]

We have the following main tasks:

• A better testing framework: Tests each PR with actual training (and record performances). Unit test for smaller components.
• More modalities support: Merge audio support branch. Add support for more branches.
• Diffusion language model support: Support for RL tuning of diffusion language models.

Each can be broken down into smaller tasks:

• A better testing framework: Tests each PR with actual training (and record performances). Unit test for smaller components.

  • CI/CD Pipeline Setup
    • Configure GitHub Actions for automated testing on PRs
    • Set up GPU runners for training tests
    • Implement performance benchmarking and tracking
    • Set up automatic performance regression alerts
  • Unit Testing
    • Write unit tests for data loading and preprocessing modules
    • Test reward computation components
    • Test loss calculation functions
    • Validate model initialization and checkpointing
    • Test distributed training utilities
    • Test memory management and cleanup
    • Validate gradient computation and backpropagation

• More modalities support: Merge audio support branch. Add support for more modalities.

  • Audio Integration
    • Merge existing audio support branch
    • Add example training scripts for audio tasks
    • Create audio quality evaluation metrics
  • Arbitrary Modality Framework
    • Design generic modality interface/base classes
    • Create modality fusion layers
    • Implement cross-modal attention mechanisms
    • Build modality-agnostic data loaders
    • Design modality registration system
    • Create modality mixing strategies for training

• Diffusion language model support: Support for RL tuning of diffusion language models.

  • Architecture Integration
    • Implement diffusion model base classes
    • Add noise scheduling modules (linear, cosine, etc.)
    • Create denoising loss functions
    • Integrate with existing model registry
    • Implement score matching objectives
    • Add variational bounds computation
  • Training Pipeline
    • Adapt PPO for diffusion models
    • Implement diffusion-specific sampling strategies
    • Create hybrid autoregressive-diffusion training
    • Add diffusion-specific evaluation metrics
    • Implement training stability techniques
    • Create checkpointing for diffusion models
  • RL Algorithms for Diffusion
    • Adapt reward modeling for continuous outputs
    • Implement diffusion-specific PPO variants
    • Create direct preference optimization for diffusion
    • Design curriculum learning strategies
    • Implement trajectory optimization methods
    • Add support for conditional generation with RL