强化学习计算成本优化方案
Rollouts drive most of RL's compute cost. But splitting rollout and training across engines risks nu...
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Fireworks公司通过协同构建引擎，解决了强化学习中rollout和训练分离带来的计算和一致性问题。
Fireworks公司提出强化学习计算优化方案。Rollouts过程消耗了强化学习大部分计算成本。分离rollout和训练引擎会导致数值不匹配问题。在MoE模型中，这种分离甚至可能将token发送到不同的专家模型。
原文 · Fireworks AI
Rollouts drive most of RL's compute cost. But splitting rollout and training across engines risks nu...
Rollouts drive most of RL's compute cost. But splitting rollout and training across engines risks numerical mismatches, and in MoE models, that can even send tokens to different experts. We co-build both engines at Fireworks, so training stays fast and consistent. Learn more: bit.ly/4ALjga5 💬 0 🔄 0 ❤️ 0 👀 88 ⚡
