allenai/RL4LMs

Health v2 · maintenance only

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2394 stars · 201 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

RL4LMs is a modular Python library from AllenAI for fine-tuning language models with reinforcement learning to align with human preferences. It provides customizable building blocks including on-policy algorithms (PPO, A2C, TRPO, NLPO), reward functions, NLG metrics, datasets, and actor-critic policies for causal and seq2seq LMs.

Use cases

• fine-tune language models with reinforcement learning
• train LMs to optimize reward functions like ROUGE or sentiment scores
• run RLHF-style training on summarization or translation tasks
• benchmark on-policy RL algorithms on NLP generation tasks
• apply PPO or NLPO to GPT-2 or T5 models
• optimize text generation models on custom datasets

When to choose

• you want to fine-tune causal or seq2seq LMs with RL algorithms like PPO or NLPO
• you need a library of NLG metrics usable as reward functions
• you want a tested, benchmarked framework for RL-based text generation experiments

When to avoid

• you need modern large-scale RLHF infrastructure for instruction-tuned chat models
• you only need supervised fine-tuning without reinforcement learning
• you require active development and support for the latest LLM architectures

Facets

library · maturity maintenance

machine-learning reinforcement-learning llm-training nlp benchmarking reinforcement-learning machine-learning large-language-models python rlhf text-generation ppo nlpо reward-functions fine-tuning human-preferences actor-critic natural-language-processing

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