RL4LMs is a modular reinforcement learning library from the Allen Institute for AI (AI2) for fine-tuning language models toward human preferences. It provides the environments, algorithms, and evaluation tooling needed to train text generation models with RL instead of plain supervised learning. The project has earned more than 2,300 stars on GitHub and was last actively updated in March 2024.
Supervised fine-tuning teaches a model to imitate examples, but many qualities people care about โ helpfulness, factuality, summary quality โ are easier to express as a reward than as demonstrations. RL4LMs wraps language generation tasks in Gym-style environments so standard RL algorithms can optimize models against reward functions and preference-based metrics. It covers tasks such as summarization, dialogue generation, machine translation, and table-to-text generation, and ships with a library of automatic metrics and reward components that can be combined for different goals. Being built for research, it emphasizes modularity: environments, policies, algorithms, and rewards can each be swapped and compared. It also includes tooling for tracking and visualizing training progress across runs, which matters in practice because small changes to a reward function can swing generation quality in unexpected ways on different downstream tasks.
RL4LMs is completely free and open source under the Apache-2.0 license. The only costs are the compute resources you use for training runs.
RL4LMs is for NLP researchers and ML engineers studying preference-based fine-tuning and RLHF techniques. It is a solid foundation for experiments where you need to compare algorithms, rewards, and tasks in a controlled, reproducible setup.