Victoria X Lin @VictoriaLinML
Research Scientist @AIatMeta | MoMa/MoTš¼ ⢠RA-DITš ⢠Llama4š¦ Ex: @SFResearch ⢠PhD @uwcse š https://t.co/j6QTac5q0r š“ Bay Area victorialin.org Menlo Park, CA Joined December 2010-
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OK, @sarawiltberger and I are experimenting with a small, project-based mentorship program designed for the age of AI. Weāre looking for resourceful self-startersāfromĀ early high school to early-career professionalsāwho want to prove their abilities through hard work. YouĀ donātā¦
OK, @sarawiltberger and I are experimenting with a small, project-based mentorship program designed for the age of AI. Weāre looking for resourceful self-startersāfromĀ early high school to early-career professionalsāwho want to prove their abilities through hard work. YouĀ donātā¦
š„ We introduce Multiverse, a new generative modeling framework for adaptive and lossless parallel generation. š Multiverse is the first open-source non-AR model to achieve AIME24 and AIME25 scores of 54% and 46% š Website: multiverse4fm.github.io š§µ 1/n
So this is not a benchmark for software engineering agents. Itās meant to test core reasoning and intelligence through codingābacked by 71 pages of deep analysis from some of the best competitive programmers out there. This effort was carried out by students across multipleā¦
So this is not a benchmark for software engineering agents. Itās meant to test core reasoning and intelligence through codingābacked by 71 pages of deep analysis from some of the best competitive programmers out there. This effort was carried out by students across multipleā¦
@VictoriaLinML splitting transformer parameters byāUnderstanding (Xātext) vs. š·Generation (Xāimage) functionality. We already did that in LMFusion
Let's talk about Mixture-of-Transformers (MoT) and heterogeneous omni-model training. 1. Inspired by prior architectures consisting of modality-specific parametersāsuch as Flamingo, CogVLM, BEIT-3, and MoMAāMoT (arxiv.org/abs/2411.04996) pushes this idea further by usingā¦
Let's talk about Mixture-of-Transformers (MoT) and heterogeneous omni-model training. 1. Inspired by prior architectures consisting of modality-specific parametersāsuch as Flamingo, CogVLM, BEIT-3, and MoMAāMoT (arxiv.org/abs/2411.04996) pushes this idea further by usingā¦
We don't often see prep thread for paper announcement on X, but this mini crash course on the information capacity of LLM is well worth checking out
We don't often see prep thread for paper announcement on X, but this mini crash course on the information capacity of LLM is well worth checking out
Surprising result! Spurious rewards -- even random rewards -- boost RLVR performance on Qwen models ā but not on OLMo or others. The paper explores some hypotheses, but itās still unclear why. Key takeaway: always validate across base models when probing reasoning with RLVR.
Surprising result! Spurious rewards -- even random rewards -- boost RLVR performance on Qwen models ā but not on OLMo or others. The paper explores some hypotheses, but itās still unclear why. Key takeaway: always validate across base models when probing reasoning with RLVR.
ByteDance | Seed has been consistently impressive over the past few months, publishing some truly insightful papers. BAGEL is one of them. I learned a lot from reading it. A few key takeaways: - Embedded "thinking" directly into native media generation, proving its effectivenessā¦
ByteDance | Seed has been consistently impressive over the past few months, publishing some truly insightful papers. BAGEL is one of them. I learned a lot from reading it. A few key takeaways: - Embedded "thinking" directly into native media generation, proving its effectivenessā¦
the scale of data collection in the AI labs pales in comparison to 2010s google itās mostly web scraping and data-labeling. compare that to diligently photographing streets of every country, mapping earth via satellite, scanning every book known to man.. now *that* was ambitious
I am pleased to announce a new version of my RL tutorial. Major update to the LLM chapter (eg DPO, GRPO, thinking), minor updates to the MARL and MBRL chapters and various sections (eg offline RL, DPG, etc). Enjoy! arxiv.org/abs/2412.05265
This is really cool work! I wonder if we could generalize even better by introducing modality as feature embedding to the router instead. That is router gets privileged information.
This is really cool work! I wonder if we could generalize even better by introducing modality as feature embedding to the router instead. That is router gets privileged information.
š Excited to share: "šš¢š±šš®š«š-šØš-šš«šš§š¬ššØš«š¦šš«š¬ (ššØš)" has been officially accepted to TMLR (March 2025) and the code is now open-sourced! š GitHub repo: github.com/facebookresear⦠š Paper: arxiv.org/abs/2411.04996 How can we reduce pretraining costs forā¦
Meet ReasonIR-8BāØthe first retriever specifically trained for reasoning tasks! Our challenging synthetic training data unlocks SOTA scores on reasoning IR and RAG benchmarks. ReasonIR-8B ranks 1st on BRIGHT and outperforms search engine and retriever baselines on MMLU and GPQAš„
We should host more top ML conferences (ICLR, ICML, NeurIPS) in Asia
Our previous work showed that šš«šššš¢š§š šÆš¢š¬š®šš„ šš”šš¢š§āšØšāšš”šØš®š š”šš¬ šÆš¢š ššØšØš„ š®š¬š significantly boosts GPTā4oās visual reasoning performance. Excited to see this idea incorporated into OpenAIās o3 and o4āmini models (openai.com/index/thinkingā¦).ā¦
Our previous work showed that šš«šššš¢š§š šÆš¢š¬š®šš„ šš”šš¢š§āšØšāšš”šØš®š š”šš¬ šÆš¢š ššØšØš„ š®š¬š significantly boosts GPTā4oās visual reasoning performance. Excited to see this idea incorporated into OpenAIās o3 and o4āmini models (openai.com/index/thinkingā¦).ā¦
Our Llama 4ās industry leading 10M+ multimodal context length (20+ hours of video) has been a wild ride. The iRoPE architecture Iād been working on helped a bit with the long-term infinite context goal toward AGI. Huge thanks to my incredible teammates! šLlama 4 Scout š¹17Bā¦
Introducing our first set of Llama 4 models! Weāve been hard at work doing a complete re-design of the Llama series. Iām so excited to share it with the world today and mark another major milestone for the Llama herd as we release the *first* open source models in the Llama 4ā¦
and yet david burdeny still did it better in 2007 with a camera
Introducing DRAMAš: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers. We propose to train a smaller dense retriever using a pruned LLM as the backbone, fine-tuned with diverse LLM data augmentations. With single-stage training, DRAMA achieves strongā¦

AK @_akhaliq
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Jacob Andreas @jacobandreas
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Delip Rao e/Ļ @deliprao
61K Followers 5K Following Busy inventing the shipwreck. @Penn. Past: @johnshopkins, @UCSC, @Amazon, @Twitter ||Art: #NLProc, Vision, Speech, #DeepLearning || Life: éå , improv, running š
Tim Dettmers @Tim_Dettmers
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Mike Lewis @ml_perception
8K Followers 242 Following Llama3 pre-training lead. Partially to blame for things like the Cicero Diplomacy bot, BART, RoBERTa, kNN-LM, top-k sampling & Deal Or No Deal.
Sewon Min @sewon__min
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Behnam Neyshabur @bneyshabur
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AI at Meta @AIatMeta
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Jim Fan @DrJimFan
325K Followers 3K Following NVIDIA Director of Robotics & Distinguished Scientist. Co-Lead of GEAR lab. Solving Physical AGI, one motor at a time. Stanford Ph.D. OpenAI's 1st intern.
Yi Tay @YiTayML
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Jason Wei @_jasonwei
98K Followers 636 Following ai researcher @meta superintelligence labs, past: openai, google š§
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77K Followers 2K Following a combination of a mediocre scientist, a mediocre manager, a mediocre advisor & a mediocre physicist at @nyuniversity (@CILVRatNYU) & @PrescientDesign
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Richard Socher @RichardSocher
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Jacob Andreas @jacobandreas
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Sam Bowman @sleepinyourhat
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Tim Dettmers @Tim_Dettmers
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Mike Lewis @ml_perception
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Yoav Artzi @yoavartzi
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45K Followers 1K Following AI Developer Experience @GoogleDeepMind | prev: Tech Lead at @huggingface, AWS ML Hero š¤ Sharing my own views and AI News š§š»āš» https://t.co/7IosdlNz22
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210K Followers 332 Following The Stanford Artificial Intelligence Laboratory (SAIL), a leading #AI lab since 1963. āµļøš¤ Emmy-winning video: https://t.co/lV9smZTC1m
Jiawei Zhao @jiawzhao
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Shaojie Bai @shaojieb
1K Followers 285 Following Doing AI at @thinkymachines. Previously GenAI+RLR @ Meta. CMU MLD. Twitter account for more than AI.
Alexander Kirillov @_alex_kirillov_
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Song Mei @Song__Mei
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Agrim Gupta @agrimgupta92
6K Followers 320 Following Simulating reality @GoogleDeepMind | co-created: Veo2, Genie3, WALT | prev PhD @Stanford
Jack Parker-Holder @jparkerholder
9K Followers 779 Following Co-lead of Genie 3 @GoogleDeepMind & Honorary Associate Professor @UCL_DARK. Dad (š¶š¶), CFC fan, BJJ. Views are my own :)
Zhuohan Li @zhuohan123
9K Followers 865 Following mts @ openai | cs phd @ š uc berkeley | building @vllm_project | machine learning system | the real agi is the friends we made along the way
Sherwin Wu @sherwinwu
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5K Followers 256 Following Home of the first annual reinforcement learning conference. Stay tuned up for updates!
Bob McGrew @bobmcgrewai
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Bowen Cheng @bowenc0221
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720 Followers 101 Following research @GoogleDeepMind. co-developed gemini deep think. co-led model training for IMO š„ | prev: RL PhD at @StanfordAILab
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15K Followers 492 Following Making things, trail running. Prev: Codex @OpenAI, https://t.co/4qWGncHOAX, co-founder @Segment, @MIT
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357 Followers 37 Following Efficient Systems for Foundation Models Workshop, ICML2025. Join us if you are interested in the challenges associated with large models training & inference!
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Jiahui Yu @jhyuxm
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Hunyuan @TencentHunyuan
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Toby Pohlen @TobyPhln
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Xiaohua Zhai @XiaohuaZhai
11K Followers 311 Following Researcher at Meta (previously at OpenAI Zürich, Google DeepMind)
Charles Qi @charles_rqi
10K Followers 273 Following Autopilot and AI @Tesla | Prev: Research Scientist & Manager @Waymo | Postdoc @metaai (FAIR), PhD @Stanford
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Ashish Vaswani @ashVaswani
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Aleksander Holynski @holynski_
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Jerry Tworek @MillionInt
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Ricky T. Q. Chen @RickyTQChen
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Yuval Kirstain @YKirstain
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Yi Wu @jxwuyi
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