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Agents struggle to act effectively in 3D scenes, with none of the eleven evaluated VLMs achieving consistent performance across diverse tasks.
Predictive divergence masks significantly enhance the stability of RL updates in LLMs, outperforming traditional methods by aligning direction criteria with actual divergence changes.
Smooth gradient adjustments in DRPO prevent harmful policy shifts, leading to more stable and efficient LLM training.
AgentSPEX transforms how we build and manage LLM-agent workflows, offering a modular and interpretable approach that outperforms traditional frameworks.
