Fireworks details keeping RL rollout and training numerically consistent
Original titleRollouts drive most of RL's compute cost. But splitting rollout and training across engines risks numerical mismatches, and in MoE models...
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Rollouts account for most of RL's compute cost, and splitting them from training across separate engines can introduce numerical mismatches. In MoE models, such mismatches can even route tokens to different experts. Fireworks says it co-builds both engines so training stays fast and consistent.
Source: Fireworks · x.comPublished · added here