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EvoSyn is proposed, a learning-feedback-driven multi-agent framework that organizes synthesis around a revisable state specifying what interaction factors can be deliberately generated and where synthesis effort should be allocated.
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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