RL4LMs (Reinforcement Learning for Language Models) is an open-source framework developed by the Allen Institute for AI (AI2) that enables researchers and developers to train, evaluate, and benchmark language models using Reinforcement Learning with Human Feedback (RLHF). It is designed to accelerate experimentation in alignment, reward modeling, and policy optimization for large language models (LLMs).

The platform provides a standardized interface for integrating various RL algorithms with popular LLMs such as GPT-2, GPT-Neo, and OPT, and supports custom reward functions, feedback datasets, and fine-tuning protocols.

Key benefits:

• Modular and extensible RLHF framework for LLM research

• Supports multiple models and RL algorithms

• Built-in tasks, evaluation metrics, and dataset loaders

What are the main features of RL4LMs?

Modular framework for RLHF on LLMs

RL4LMs is built with flexibility in mind, allowing users to experiment with different RL methods and architectures.

• Plug-and-play support for Proximal Policy Optimization (PPO), DPO, and others

• Integrates with Hugging Face Transformers and Accelerate

• Works with reward functions based on human preferences, classifiers, or heuristic rules

Predefined tasks and evaluation setups

The framework includes a suite of language tasks that reflect real-world applications.

• Summarization, dialogue generation, and question answering

• Metrics for helpfulness, toxicity, and factual accuracy

• Tools for zero-shot and few-shot evaluation

Custom reward modeling and tuning

Users can define their own reward functions or load pretrained ones for different use cases.

• Support for reward modeling from human-labeled data

• Compatibility with open datasets such as Anthropic HH and OpenAssistant

• Tools for scaling up reward model training across tasks

Baseline policies and reproducible benchmarks

RL4LMs includes reference implementations of baseline policies and reproducible training scripts.

• Preconfigured training pipelines for PPO and supervised fine-tuning

• Easy comparison between different reward functions and policy updates

• Logging and checkpointing tools for experimental tracking

Community-driven and open research focus

Developed as part of the AllenNLP ecosystem, RL4LMs is open to contributions and geared toward academic transparency.

• Open-source under Apache 2.0 license

• Designed for research in safe, aligned, and controllable language models

• Actively maintained by the Allen AI community

Why choose RL4LMs?

• Research-ready RLHF platform, designed for studying alignment and optimization in LLMs

• Supports experimentation across tasks, models, and reward structures

• Extensible and open, compatible with common ML libraries and datasets

• Promotes reproducibility and transparency, ideal for academic work

• Backed by AI2, with a focus on safe and responsible AI development