Cursor | The Hidden Bug in Every Large-Scale RL Run
Sequoia Capital248 字 (约 1 分钟)
75
In large-scale RL training, numerical mismatches arise due to model version drift and floating-point precision differences, causing inconsistent log probabilities during inference and introducing training bias.
入选理由：在异步训练中，需重运行前向传播以生成对数概率，但相同模型版本下结果可能不同。
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