RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation

Efficient text-to-image generation requires both reinforcement-learning (RL)-based reward alignment and few-step distillation, yet these procedures are typically performed sequentially, increasing training cost and risking the loss of reward gains during compression. We instead take an RL-native perspective: diffusion RL already generates reward-scored finite-step trajectories, whose intermediate states provide a natural source of distillation supervision rather than a disposable byproduct of sa

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ethics.ai (10 August 2026), “RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation,” evidence record 18022, https://ethics.ai/record/18022 (originally published by arXiv).

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