This project implements a highly modular, production-grade Reinforcement Learning (RL) trainer for MLX-based language models, designed from the ground up with best software engineering practices.

• SOLID & OOP: Every component is designed with Single Responsibility, Open/Closed principles, and Dependency Injection in mind, using Abstract Base Classes (ABCs) to define clear contracts. This ensures code is testable, maintainable, and extensible.
• Meta-Programming (Registry Pattern): Reward functions and evaluators are pluggable components. They can be added to the system without modifying any core training code, simply by decorating the class and updating a YAML configuration file. This adheres to the Open/Closed Principle.
• Predictable Code (Pydantic): All configurations are rigorously validated through Pydantic schemas, ensuring type safety, data integrity, and preventing runtime errors stemming from malformed or incomplete configurations. This makes the system robust and easy to reason about.
• Efficient I/O (Asyncio & Aiofiles): The data pipeline leverages Python's asyncio framework in conjunction with aiofiles for non-blocking asynchronous file operations. This is crucial for handling large datasets efficiently without blocking the event loop.
• Safe & Isolated Execution (Multiprocessing): The code_execution reward function uses Python's multiprocessing library (specifically the spawn context) to run untrusted code in a separate, isolated process with strict timeouts. This prevents the main training loop from crashing and ensures system stability.
• Robustness (Custom Exceptions): A hierarchy of custom, domain-specific exceptions is used throughout the codebase for predictable, granular error handling. This allows for precise error management and recovery strategies.
• Developer-Friendly (Tqdm & Rich Logging): Integrated tqdm for clear progress bars in CLI and rich for enhanced, structured, and colorful logging output, improving developer experience and debugging.

mlx_rl_trainer/
├── configs/                # Configuration files (YAML)
├── docs/                   # Project documentation
├── scripts/                # Entry-point scripts
├── src/
│   └── mlx_rl_trainer/
│       ├── core/           # Core abstractions: config, trainer interface, managers
│       ├── algorithms/     # RL algorithm implementations (e.g., GRPO, PPO)
│       ├── data/           # Data loading, processing, batching
│       ├── evaluation/     # Benchmark evaluators
│       ├── generation/     # Text generation utilities
│       ├── monitoring/     # Logging, metrics, W&B integration
│       ├── rewards/        # Pluggable reward functions (meta-programming)
│       └── utils/          # General utilities, logging setup
└── tests/                  # Unit and integration tests

This project is designed to be immediately runnable even without a full MLX-LM model. The ModelManager and DatasetManager include mock implementations that adhere to the defined interfaces.

1. Install the package in editable mode with development dependencies:

   pip install -e ./mlx_rl_trainer[dev]

2. Run the training script: The script will automatically create dummy model and data files for the initial run.

   mlx-train --config mlx_rl_trainer/configs/experiments/code_gen_base.yaml --log-level INFO

   Observe the rich logging output, progress bars, and the simulated training loop. The first run will create models/mock_model and data/ directories.

To transition from the mock setup to full MLX-LM capabilities:

• Replace the mock implementations in mlx_rl_trainer/src/mlx_rl_trainer/core/model_manager.py and mlx_rl_trainer/src/mlx_rl_trainer/algorithms/grpo/grpo_trainer.py (specifically in generate_rollouts) with actual mlx_lm calls.
• This architecture ensures that these integrations can be done with minimal impact on other components.