allenai/RL4LMs
A reinforcement learning library for fine-tuning language models to optimize human preference reward functions.
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RL4LMs provides modular building blocks for training language models with reinforcement learning, including on-policy algorithms (PPO, A2C, TRPO, NLPO), reward functions, and 20+ NLG metrics. It supports causal LMs (GPT-2/3) and seq2seq LMs (T5, BART) across NLP tasks including summarization, translation, dialogue generation, and question answering. The library has been benchmarked across 2000+ experiments on the GRUE benchmark.
Frequently asked
- What is allenai/RL4LMs?
- A reinforcement learning library for fine-tuning language models to optimize human preference reward functions.
- Is RL4LMs open source?
- Yes — allenai/RL4LMs is open source, released under the Apache-2.0 license.
- What language is RL4LMs written in?
- allenai/RL4LMs is primarily written in Python.
- How popular is RL4LMs?
- allenai/RL4LMs has 2.4k stars on GitHub.
- Where can I find RL4LMs?
- allenai/RL4LMs is on GitHub at https://github.com/allenai/RL4LMs.