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Chinese Academy of Sciences
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GAPrompt++ is proposed, a multi-granular geometry-aware prompting method that provides richer geometric guidance for efficient 3D task adaptation and achieves state-of-the-art performance among prompting-based PEFT methods and even surpasses full fine-tuning across diverse benchmarks, while requiring less than 2% trainable parameters.
Temporal GRPO reveals that aligning reinforcement learning updates with task stages can significantly boost both efficiency and success rates in complex vision-language-action tasks.
