materl 0.1.1

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🔥 materl - A Declarative RL Library for Fast Experimentation

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materl is a Reinforcement Learning library designed for rapid experimentation with language models. It combines a clean, declarative API with an accelerated backend, allowing you to focus on algorithm logic instead of boilerplate.

It's built for researchers who want to test a new reward function, tweak a loss calculation, or implement a novel algorithm quickly and efficiently.

✨ Philosophy: From Idea to Result, Faster

materl is built for iterating quickly. The design is centered on simplicity and performance at the point of experimentation.

• Declarative & Functional: Define your entire RL workflow as a series of functional steps. This makes experiments easy to read, modify, and reproduce.
• Performant by Default: The library is designed to be fast. Performance-critical sections are handled by an optimized backend, so you get great speed without writing low-level code.
• Minimalist API: The API is intentionally simple. Core concepts like Agent, Recipe, and compile are all you need to get started, reducing cognitive overhead.

🏗️ Architecture: Your Experiment as a Graph

The core of materl is its declarative, graph-based paradigm. A "recipe" is a Python function that defines the sequence of operations in your experiment.

1. Agents: Simple wrappers around your models (e.g., from Hugging Face Transformers).
2. Recipe: A function that describes the steps: generate text, calculate log-probabilities, compute rewards, and define the loss.
3. Symbolic Graph: The recipe returns a lightweight data structure that represents your entire workflow.
4. Compiler: The compile() function processes this graph and prepares it for execution.
5. Execution: Calling .run() on the compiled graph executes the experiment.

🚀 A Simple DAPO Experiment

This example shows how to set up and run a DAPO experiment. The code reads like a description of the experimental procedure itself.

from materl.agents import Agent
from materl.compiler import compile
from materl.config import GenerationConfig, DAPOConfig
from materl.recipes import dapo
import torch

# 1. Set up your models using the Agent wrapper
model_name = "gpt2"
device = "cuda" if torch.cuda.is_available() else "cpu"
policy_agent = Agent(model_name, trainable=True, device=device)
ref_agent = Agent(model_name, trainable=False, device=device)

# 2. Define your inputs and configurations
prompts = ["Hello, my name is", "What is the capital of France?"]
gen_config = GenerationConfig(max_completion_length=50)
algorithm_config = DAPOConfig(beta=0.1)

# 3. Use a recipe to create a symbolic graph of your experiment
symbolic_graph = dapo(
    policy=policy_agent,
    ref_policy=ref_agent,
    prompts=prompts,
    max_completion_length=gen_config.max_completion_length,
)

# 4. Compile the graph and run the experiment
compiled_graph = compile(symbolic_graph)
final_context = compiled_graph.run(
    policy=policy_agent,
    ref_policy=ref_agent,
    prompts=prompts,
    generation_config=gen_config,
    dapo_config=algorithm_config,
)

print("✅ DAPO experiment finished successfully!")
print(f"Final context keys: {list(final_context.keys())}")

🧪 Included Recipes

materl comes with several pre-built recipes to get you started:

• GRPO (Group Relative Policy Optimization)
• DAPO (Decoupled Advantage Policy Optimization)
• VAPO (Value-Aligned Policy Optimization)

You can find these in materl/recipes and see them in action in the examples/ directory. Creating your own recipe is as simple as writing a new Python function.

🔭 Future Direction

Our goal is to make materl the best tool for applied RL research and fast prototyping. We plan to:

• Expand the Recipe Book: Add more state-of-the-art algorithms.
• Enhance Debugging Tools: Provide tools to inspect and visualize the computational graph.
• Broaden Hardware Support: Continue to optimize performance across a wider range of GPUs.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

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Ready to accelerate your RL training? Get started with materl today! 🚀