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Lab of CAD & CG, Zhejiang University, State, Ant Group
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Most self-distillation methods for agentic RL fail to consistently outperform a strong baseline, and impressive final scores often hide unstable training dynamics and substantial computational overhead.
Foundation models struggle with spatial tasks, achieving only 12% success in reproducing target viewpoints, but a novel post-training framework boosts performance to over 51%.