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Adina Yakefu
AdinaY
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SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
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π Excited to share that our paper, "SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models", has been accepted to #ICRA2025! π Preprint: https://arxiv.org/pdf/2409.19471 We introduce SELP (Safe Efficient LLM Planner), a novel approach for generating plans that adhere to user-specified constraints while optimizing for time-efficient execution. By leveraging linear temporal logic (LTL) to interpret natural language commands, SELP effectively handles complex commands and long-horizon tasks. π€ π‘SELP presents three key insights: 1οΈβ£ Equivalence Voting: Ensures robust translations from natural language instructions into LTL specifications. 2οΈβ£ Constrained Decoding: Uses the generated LTL formula to guide the autoregressive inference of plans, ensuring the generated plans conform to the LTL. 3οΈβ£ Domain-Specific Fine-Tuning: Customizes LLMs for specific robotic tasks, boosting both safety and efficiency. π Experiment: Our experiments demonstrate SELPβs effectiveness and generalizability across diverse tasks. In drone navigation, SELP outperforms state-of-the-art LLM planners by 10.8% in safety rate and by 19.8% in plan efficiency. For robot manipulation, SELP achieves a 20.4% improvement in safety rate. @yiwu @jiang719 #ICRA2025 #LLM #Robotics #Agent #LLMPlanner
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Xwen π₯ a series of open models based on Qwen2.5 models, developed by a brilliant research team of PhD students from the Chinese community. https://huggingface.co/collections/shenzhi-wang/xwen-chat-679e30ab1f4b90cfa7dbc49e β¨ 7B/72B β¨ Apache 2.0 β¨ Xwen-72B-Chat outperformed DeepSeek V3 on Arena Hard Auto
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A Short Summary of Chinese AI Global Expansion
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Oct 1, 2024
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