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Speculative decoding reduces large language model inference latency by drafting multiple tokens before target-model verification, but its effectiveness depends on both the proposal mechanism and draft depth. Fixed configurations cannot respond to changes in predictability, repetition, and acceptance during generation,...
CLEAR first employs a reflection agent to perform contrastive analysis over past execution trajectories and summarize useful context for each observed task to train a context augmentation model (CAM), which further optimize CAM using reinforcement learning, where the reward signal is obtained by running the task execut...
Across four RoboTwin tasks spanning different horizons and coordination patterns, Prism-GRPO improves success and quality at matched rollout budgets and reaches target success rates with up to 56% fewer rollouts.
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