讓 Token 持續流動：來自 16 個開源強化學習（RL）函式庫的啟示
Original: Keep the Tokens Flowing: Lessons from 16 Open-Source RL Libraries
With the success of reasoning models such as DeepSeek-R1, reinforcement learning (RL/RLHF) has become a critical technique for improving…
隨著 LLM 強化學習（RL）成為顯學，如何優化訓練效率成為關鍵。Hugging Face 評估了 16 個開源 RL 函式庫，指出傳統同步訓練因「生成」與「訓練」運算特性不同，會導致嚴重的 GPU 閒置。本文總結了非同步 RL 訓練（Async RL）的最新技術趨勢，探討如何透過解耦架構與高效記憶體管理，讓 Token 持續流動並最大化吞吐量。
With the success of reasoning models such as DeepSeek-R1, reinforcement learning (RL/RLHF) has become a critical technique for improving the alignment and reasoning capabilities of large language models (LLMs). However, in engineering practice, using RL to train LLMs presents enormous efficiency challenges. This technical blog post from Hugging Face provides an in-depth analysis of the architectures of 16 mainstream open-source RL libraries — including TRL, OpenRLHF, VeRL, and trlX — and explores how to address performance bottlenecks in RL training.
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