TLDR Tech / Practical
Mixed-precision, mixed-accelerator samplers show promise for large models
A new study shows that reinforcement learning for large models can operate reliably with mixed-precision and mixed-accelerator samplers. This is achieved by using off-policy correction methods such as masked importance sampling, score centering, and top-p sampling with mask replay. By separating the samplers from the training process, this configuration matches the agent coding capabilities of homogeneous samplers while cutting sampling expenses by approximately 15%.
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