RL4LMs

Why we included this project

RL4LMs provides the building blocks for reinforcement-learning fine-tuning of language models, so you can focus on the reward and policy rather than the loop plumbing. You pick an on-policy algorithm like PPO or NLPO, a reward function from the library's broad set of NLG metrics, and a causal or seq2seq model as the policy. Predefined YAML configs make runs reproducible, and the library has been benchmarked across tasks like summarization, translation, dialogue, and table-to-text generation. It's a solid starting point for RLHF-style experiments or custom reward design, though the code is research-oriented and last updated in early 2024, so expect to adapt it for production.