Jian Wang
@jwanglvy
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Postdoc & PhD @HongKongPolyU, Prev. Visiting @UMich @SLED_AI. | NLP, Conversational AI, Embodied Intelligence.
Hong Kong
Joined August 2017
RT @SoYeonTiffMin: 🚨🚨 Preprint Alert 🚨🚨 🚀🚀 As AI become agents 🤖, how can we reliably delegate tasks to them, if they cannot communicate t…
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RT @xiangyue96: Demystifying Long CoT Reasoning in LLMs Reasoning models like R1 / O1 / O3 have gained massive atte…
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Very impressive paper!
If you can only read one DeepSeek paper in your life, read DeepSeek Math. Everything else is either ≈obvious in hindsight or clever optimization. DeepSeek Math is a tour de force of data engineering, general DL LLM methodology, RL, and just beautiful. Just 22 pages.
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@ziqiao_ma Really inspiring, too! "not enough compute" should not be a limitation; Academia can do many more things than I expect.
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RT @jiayi_pirate: We reproduced DeepSeek R1-Zero in the CountDown game, and it just works Through RL, the 3B base LM develops self-verifi…
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RT @deepseek_ai: 🚀 DeepSeek-R1 is here! ⚡ Performance on par with OpenAI-o1 📖 Fully open-source model & technical report 🏆 MIT licensed: D…
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Welcome to @hemingkx 's awesome tutorial on Speculative Decoding #COLING2025 ！
Is your LLM inference fast enough🧐? Welcome to our #COLING2025 tutorial "Speculative Decoding for Efficient LLM Inference" with Yongqi Li, Qian Liu @sivil_taram, Cunxiao Du, and Wenjie Li Jan 19: 9:00-12:30 (GST) Loc: Capital Suite 7 Website:
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RT @xingyaow_: I have a theory: the amount of information provided in the context differs significantly for these two types of agent scaffo…
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RT @lifan__yuan: How to unlock advanced reasoning via scalable RL? 🚀Introducing PRIME (Process Reinforcement through Implicit Rewards) and…
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RT @ManlingLi_: [Long Tweet Ahead] Faculty Interview Tips & Common Questions: 🧘♀️0. Firstly, do not be nervous - Almost everything can…
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RT @sunjiao123sun_: Mitigating racial bias from LLMs is a lot easier than removing it from humans! Can’t believe this happened at the bes…
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🤗Welcome to check out our #EMNLP2024 Findings work on "Exploration-based Error Correction Learning" for embodied agents, an effective method to bridge the gap between agents' intrinsic knowledge and environmental knowledge, e.g., physical constraints.
🌟 Excited to share our #EMNLP2024Findings work: E2CL: Exploration-based Error Correction Learning for Embodied Agents, a novel framework for improving embodied agents' alignment with environments! 🌟 📄 Welcome to check our work:
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RT @rose_e_wang: 🔥 Big news! 🔥 Earlier this month, we released Tutor CoPilot—an AI-powered, real-time assistant that scales expertise and t…
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AI for Science -> AI is Science!
BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.”
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RT @xingyaow_: I've been thinking about one related thing for a while: Why do humans need a division of labor (i.e., a multi-agent system)?…
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