TRL (Transformer Reinforcement Learning) is a full-stack library that allows researchers to train transformer language models and stable diffusion models with reinforcement learning. It includes tools such as SFT (Supervised Fine-tuning), RM (Reward Modeling), and PPO (Proximal Policy Optimization). TRL improves the efficiency, adaptability, and robustness of transformer language models for tasks like text generation, translation, and summarization.
Introducing TRL: Practical AI Solutions for Middle Managers
Supervised Fine-tuning (SFT), Reward Modeling (RM), and Proximal Policy Optimization (PPO) are all part of TRL, a full-stack library that provides tools to train transformer language models and stable diffusion models with Reinforcement Learning. TRL is an extension of Hugging Face’s transformers collection, allowing for easy loading of pre-trained language models. With TRL, you can:
- Tune language models or adapters on a custom dataset using SFTTrainer, a lightweight and user-friendly wrapper around Transformers Trainer.
- Modify language models for human preferences using RewardTrainer, a lightweight wrapper over Transformers Trainer.
- Optimize language models with PPOTrainer, which only requires (query, response, reward) triplets.
- Utilize transformer models with additional scalar outputs for reinforcement learning in AutoModelForCausalLMWithValueHead and AutoModelForSeq2SeqLMWithValueHead.
- Implement various practical examples, such as training GPT2 to write favorable movie reviews, creating a full RLHF using adapters, reducing toxicity in GPT-j, and more.
How does TRL work?
TRL trains a transformer language model to optimize a reward signal determined by human experts or reward models. Proximal Policy Optimization (PPO) is used to train the transformer language model, modifying its policy to improve performance. PPO fine-tunes the language model in three main ways:
- Release: The language model provides a possible sentence starter in response to a question.
- Evaluation: A function, model, or human judgment is used to assign a single numeric value to each query/response pair.
- Optimization: The language model’s log-probabilities of tokens in sequences are optimized using query/response pairs. The model is trained with a reference model and an additional reward signal that ensures generated replies are close to the reference model. PPO is used for training.
Key features of TRL
TRL offers several advantages over conventional approaches to training transformer language models:
- TRL can train transformer language models for a wide range of tasks beyond text creation, translation, and summarization.
- Training with TRL is more efficient than supervised learning.
- TRL-trained models show improved resistance to noise and adversarial inputs.
- TextEnvironments, a new feature in TRL, allows for RL-based language transformer models to interact with tools and fine-tune performance.
TRL-trained transformer language models produce more creative and informative writing, perform better in translation tasks, and provide more precise and concise text summarization compared to models trained with conventional methods.
For more details, visit the TRL GitHub page.
Introducing TextEnvironments in TRL 0.7.0!
TextEnvironments in TRL allow language models to use tools to solve tasks more reliably. Models trained with TextEnvironments can utilize resources like Wiki search and Python to answer trivia and math questions. Check out the Twitter post for a demonstration.
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