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Accelerate RL rollouts by up to 50% with distribution-aware speculative decoding
This article describes a technique for accelerating reinforcement learning (RL) rollouts using distribution-aware speculative decoding, which can achieve up to 50% speedup improvements. The method lik
This article describes a technique for accelerating reinforcement learning (RL) rollouts using distribution-aware speculative decoding, which can achieve up to 50% speedup improvements. The method likely optimizes token generation during RL training by leveraging predictive models that are aware of the actual distribution of tokens being generated, reducing computation while maintaining quality. This approach has practical applications for improving the efficiency of RL-based language model training pipelines.
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Source: Together AI Blog | 2026-04-24