Mike Lewis @ml_perception
Llama3 pre-training lead. Partially to blame for things like the Cicero Diplomacy bot, BART, RoBERTa, attention sinks, kNN-LM, top-k sampling & Deal Or No Deal. Seattle Joined September 2019-
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@ehsanik @Vercept_ai @AnthropicAI Amazing, congrats!!
Excited to see what the amazing @sarahookr and team build here!
Beginnings are very special. Today is an important day for @adaptionlabs. Today a handful of one-size-fits-all-models are optimized for the average use case. Averages erase the exceptional. Everything intelligent adapts. So should AI.
Our team in FAIR at Meta is hiring a (full-time) researcher! We work on the topics of Reasoning, Alignment and Memory/architectures (RAM) for self-improvement & co-improvement. Apply here: metacareers.com/profile/job_de… Location: NY, Seattle or Menlo Park. Some of our recent work to give flavor: Co-Improvement (position): arxiv.org/abs/2512.05356 SPICE (Self-Play in Corpus Environments): arxiv.org/abs/2510.24684 Self-Challenging Agents: arxiv.org/abs/2506.01716 RL from Human Interaction: arxiv.org/abs/2509.25137 AggLM (parallel aggregation): arxiv.org/abs/2509.06870 StepWiser (CoT-PRM RL): arxiv.org/abs/2508.19229 DARLING (diversity-trained RL): arxiv.org/abs/2509.02534 J1 (RL-trained LLM-as-Judge): arxiv.org/abs/2505.10320 CoT-Self-Instruct: arxiv.org/abs/2507.23751 Multi-Token Attention: arxiv.org/abs/2504.00927
@edward_milsom You see this because at the start of each epoch, many of your samples were seen recently at the end of the previous epoch. By the end of your epoch, each sample hasn't been seen for at least one epoch, so is less memorized.
🚀 Introducing the Latent Speech-Text Transformer (LST) — a speech-text model that organizes speech tokens into latent patches for better text→speech transfer, enabling steeper scaling laws and more efficient multimodal training ⚡️ Paper 📄 arxiv.org/pdf/2510.06195
Love seeing these incredibly creative new evaluations! Optimizing benchmarks is easy, the real challenge is in generalizing to the tasks that don't exist yet
new post. there's a lot in it. i suggest you check it out
I've written the full story of Attention Sinks — a technical deep-dive into how the mechanism was developed and how our research ended up being used in OpenAI's new OSS models. For those interested in the details: hanlab.mit.edu/blog/streaming…
@niloofar_mire @CMU_EPP @LTIatCMU @AIatMeta @kamalikac Amazing, congrats!!
Don’t miss this - I’ve worked with Mike (@ml_perception) very closely at Meta and his talks are super informative and fun.
Want to learn about Llama's pre-training? Mike Lewis will be giving a Keynote at NAACL 2025 in Albuquerque, NM on May 1. 2025.naacl.org @naaclmeeting
📉📉NEW SCALING LAW PHENOMENON 📉📉 We find that knowledge and reasoning exhibit different scaling behaviors! Super excited to finally tell you all about our paper on the compute optimal scaling of skills: arxiv.org/pdf/2503.10061 [1/n]
✨New Preprint✨We introduce 𝐁𝐫𝐚𝐧𝐜𝐡-𝐓𝐫𝐚𝐢𝐧-𝐒𝐭𝐢𝐭𝐜𝐡 (𝐁𝐓𝐒), an efficient & flexible method for stitching together independently pretrained LLM experts (i.e. code, math) into a single, capable generalist model. Key Takeaways: ✅BTS achieves the best average generalist performance across a variety of tasks 👊 ✅We stitch together 4 x 2.7B specialized expert LLMs, where only the lightweight stitching layers (<300M params in total‼) are trained while the experts’ params remain frozen. This makes BTS super modular, flexible, and easy to train! 👊 arxiv.org/abs/2502.00075 Work done at @AIatMeta w/ @prajjwal_1, Chloe Bi, Chris Cai, @j_foerst @imjeremyhi @punitkoura, Ruan Silva, @shengs1123 @em_dinan* @ssgrn* @ml_perception* * Joint last author 🧵👇(1/5)
🚀 Introducing the Byte Latent Transformer (BLT) – An LLM architecture that scales better than Llama 3 using byte-patches instead of tokens 🤯 Paper 📄 dl.fbaipublicfiles.com/blt/BLT__Patch… Code 🛠️ github.com/facebookresear…
How can we reduce pretraining costs for multi-modal models without sacrificing quality? We study this Q in our new work: arxiv.org/abs/2411.04996 At @AIatMeta, We introduce Mixture-of-Transformers (MoT), a sparse architecture with modality-aware sparsity for every non-embedding transformer parameter (e.g., feed-forward networks, attention matrices, and layer normalization). MoT achieves dense-level performance with up to 66% fewer FLOPs! ✅ Chameleon setting (text + image generation): Our 7B MoT matches dense baseline quality using just 55.8% of the FLOPs. ✅ Extended to speech as a third modality, MoT achieves dense-level speech quality with only 37.2% of the FLOPs. ✅ Transfusion setting (text autoregressive + image diffusion): MoT matches dense model quality using one-third of the FLOPs. ✅ System profiling shows MoT achieves dense-level image quality in 47% and text quality in 75.6% of the wall-clock time** Takeaway: Modality-aware sparsity in MoT offers a scalable path to efficient, multi-modal AI with reduced pretraining costs. Work of a great team with @liliyu_lili, Liang Luo, @sriniiyer88, Ning Dong, @violet_zct, @gargighosh, @ml_perception, @scottyih, @LukeZettlemoyer, @VictoriaLinML.👏 **Measured on AWS p4de.24xlarge instances with NVIDIA A100 GPUs.
@microth @AnetteMFrank Congrats Michael!!
@apjacob03 @MIT_CSAIL @jacobandreas @KonstDaskalakis @gabrfarina @roger_p_levy @polynoamial @adamlerer @em_dinan Huge congrats Jacob!!
1/n Introducing MoMa 🖼, our new sparse early-fusion architecture for mixed-modal language modeling that significantly boosts pre-training efficiency 🚀 (arxiv.org/pdf/2407.21770). MoMa employs a mixture-of-expert (MoE) framework with modality-specific expert groups. Given any interleaved mixed-modal token sequences, each group exclusively processes tokens of the designated modality with conventional MoE routing. This is joint work with amazing co-first authors @AkshatS07, @ArmenAgha and collaborators @AIatMeta – Liang Luo, @sriniiyer88, @ml_perception, @gargighosh and @LukeZettlemoyer.
@tallinzen The base model recipe is relatively straightforward (though of course >15T tokens), so you could always use that! Post training makes a huge difference on some tasks, but not all, which is interesting in itself.
@NamanGoyal21 Can I have a nickel for every extra FLOP your work let us use?
tldr; you can go a long way in pre-training by (1) curating amazing data, (2) using a lot of FLOPs, and (3) otherwise not screwing up. All three are harder than they sound, so read the paper... That said, I'm amazed by our progress since Llama 3 - expect big things from Llama 4!
So excited for the open release of Llama 3.1 405B - with MMLU > 87, it's a really strong model and I can't wait to see what you all build with it! llama.meta.com Also check out the paper here, with lots of details on how this was made: tinyurl.com/2z2cpj8m
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