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- GitHub - huggingface trl: Train transformer language models with . . .
TRL is a cutting-edge library designed for post-training foundation models using advanced techniques like Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), and Direct Preference Optimization (DPO)
- TRL - Transformer 强化学习 - Hugging Face 文档
TRL 是一个全栈库，我们提供了一套工具，用于通过监督式微调 (SFT)、组相对策略优化 (GRPO)、直接偏好优化 (DPO)、奖励建模等方法训练 Transformer 语言模型。
- TRL - Transformer Reinforcement Learning - Hugging Face
TRL is a full stack library where we provide a set of tools to train transformer language models with methods like Supervised Fine-Tuning (SFT), Group Relative Policy Optimization (GRPO), Direct Preference Optimization (DPO), Reward Modeling, and more
- Technology readiness level - Wikipedia
TRL is determined during a technology readiness assessment (TRA) that examines program concepts, technology requirements, and demonstrated technology capabilities TRLs are based on a scale from 1 to 9 with 9 being the most mature technology [1] TRL was developed at NASA during the 1970s
- RLHF：TRL - Transformers Reinforcement Learning 使用教程 - 知乎
TRL 是huggingface中的一个完整的库，用于微调和调整大型语言模型，包括 Transformer 语言 和 扩散模型。
- Py之trl：trl (一款采用强化学习训练Transformer语言模型和稳定扩散模型的全栈库)的简介、安装、使用方法之详细攻略_trl库 . . .
trl 是一个全栈库，其中我们提供一组工具，用于通过 强化学习训练Transformer语言模型和稳定扩散模型，从监督微调步骤（SFT）到奖励建模步骤（RM）再到近端策略优化（PPO）步骤。
- HuggingFace Trl | SwanLab官方文档
TRL (Transformers Reinforcement Learning，用强化学习训练Transformers模型) 是一个领先的Python库，旨在通过监督微调（SFT）、近端策略优化（PPO）和直接偏好优化（DPO）等先进技术，对基础模型进行训练后优化。
- trl · PyPI
TRL is a cutting-edge library designed for post-training foundation models using advanced techniques like Supervised Fine-Tuning (SFT), Proximal Policy Optimization (PPO), and Direct Preference Optimization (DPO)
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