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Beijing Institute of Technology
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Existing smart home assistants miss user intentions in 60% of cases when interpreting elliptical commands, revealing a critical gap in their design.
Self-conditioning on verified trajectories boosts reinforcement learning performance by over 8%, revealing the power of internal feedback in credit assignment.
LLM agents can get 18% better at tasks by co-evolving their skills and tools, instead of learning them separately.
