Speculative Decoding for RL Training (18 minute read)
Speculative decoding is extended to RL training rollouts, preserving output distributions while speeding up sampling. The result matters for agentic systems because rollout throughput is often a bottleneck in large-scale reinforcement learning.
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Speculative decoding was applied to RL rollouts without changing output distributions, delivering up to 1.8x throughput gains and projected 2.5x end-to-end speedups at scale.
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speculative decodingreinforcement learningthroughput
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