RL4LMs: Fine-Tune Language Models with Reinforcement Learning

Overview

RL4LMs provides building blocks for fine-tuning language models with reinforcement learning, where generated text is optimized against a reward function. It brings together text-generation environments, reward and evaluation components, language-model actor-critic policies, and on-policy algorithms in a configurable workflow.

The library is aimed at researchers and practitioners experimenting with reward-driven NLP training. Its configurable datasets, rewards, and policies make it useful when existing task-specific training code is too rigid, though it is more specialized than a general-purpose language-model training toolkit.

Key Features

• Implements PPO, A2C, TRPO, and NLPO for on-policy language-model training.
• Supports actor-critic policies for causal and sequence-to-sequence language models.
• Provides reward functions based on language-generation metrics and classifier scores.
• Includes datasets and task configurations for summarization, dialogue, translation, and other text-generation tasks.
• Lets users extend datasets, rewards, metrics, algorithms, and policies, then register custom components.
• Uses YAML configurations to connect training components and experiment settings.
• Supports experiment logging, checkpoint storage, and optional Weights & Biases logging.

Use Cases

• NLP researchers comparing on-policy algorithms across text-generation tasks and reward functions.
• Model developers testing whether a custom reward improves a specific generation behavior.
• Teams adapting a training setup to their own dataset by adding a data pool and reward component.
• Practitioners benchmarking reinforcement-learning approaches against supervised language-model training.

Getting Started

git clone https://github.com/allenai/RL4LMs.git
cd RL4LMs
pip install -e .

See the README for task configurations, Docker setup, and customization guidance. The project website is rl4lms.apps.allenai.org.

Alternatives

• Verifiers: Verifiers builds custom environments for LLM training and evaluation, while RL4LMs focuses on configurable on-policy RL algorithms for text generation.

Considerations

• Reinforcement-learning fine-tuning involves model training and generation workloads, so plan for the compute and model resources those workloads require.
• Some metric computations need additional CoreNLP resources, as noted in the README.
• The repository is not marked archived, but its recorded latest push was 2024-03-01. Check the repository for current dependency compatibility and maintenance status before adopting it for a new project.
• Its focus is text generation and on-policy reinforcement learning, so it may not fit projects that need a general-purpose training framework or other optimization methods.