🐯 Liger GRPO 攜手 TRL：大幅降低 DeepSeek-R1 式強化學習訓練顯存與加速
Original: 🐯 Liger GRPO meets TRL
Since the explosive rise of DeepSeek-R1, GRPO (Group Relative Policy Optimization) has become the most widely discussed reinforcement…
Hugging Face 的 TRL 團隊宣布與 LinkedIn 開源的 Liger Kernel 整合。此合作針對當前熱門的 GRPO（群體相對策略優化）演算法進行深度優化，能顯著降低訓練時的 GPU 顯存佔用並提升吞吐量。這讓開發者在訓練類似 DeepSeek-R1 的推理模型時，能用更低的硬體門檻實現更高效的強化學習微調。
Since the explosive rise of DeepSeek-R1, GRPO (Group Relative Policy Optimization) has become the most widely discussed reinforcement learning (RL) technique for LLMs. GRPO eliminates the Critic network found in traditional PPO, significantly reducing VRAM consumption. However, in practice, the need to generate multiple outputs (rollouts) for the same prompt and evaluate them still places considerable pressure on both memory and compute.
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