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The Era of DiffusionLM might be upon us "Diffusion Beats Autoregressive in Data-Constrained Settings" they find that DiffusionLMs outperform AR models if you’re bottlenecked by data rather than FLOPs same data can be reused for 100 epochs vs 4 epochs, as dLLM learns far more!

I chatted with the MoR paper on alphaXiv, and it genuinely offers novel ideas on this, here’s one example. I probably shouldn’t say this😅, but the token‑level thinking steps of MoR with RL training would be fundamental to explore, maybe credit assignment at the token‑level of…

During #ICML2025 last week, we teamed up with @askalphaxiv to host a happy hour + panel on: "The Science Singularity - How Automated ML Research is Changing the ML Landscape" Thanks to @pzakin, @FazlBarez, @tdietterich, and @bohannon_bot for the insights - and everyone who came…

I have been waiting for this to be announced, it’s so amazing to see such elegant scaling of the Deep Think system where the same system can now achieve a gold at IMO! deepmind.google/discover/blog/…

🎉 Our paper “𝐻𝑜𝑤 𝑡𝑜 𝑇𝑟𝑎𝑖𝑛 𝑌𝑜𝑢𝑟 𝐿𝐿𝑀 𝑊𝑒𝑏 𝐴𝑔𝑒𝑛𝑡: 𝐴 𝑆𝑡𝑎𝑡𝑖𝑠𝑡𝑖𝑐𝑎𝑙 𝐷𝑖𝑎𝑔𝑛𝑜𝑠𝑖𝑠” got an 𝐨𝐫𝐚𝐥 at next week’s 𝗜𝗖𝗠𝗟 𝗪𝗼𝗿𝗸𝘀𝗵𝗼𝗽 𝗼𝗻 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗨𝘀𝗲 𝗔𝗴𝗲𝗻𝘁𝘀! 🖥️🧠 We present the 𝐟𝐢𝐫𝐬𝐭 𝐥𝐚𝐫𝐠𝐞-𝐬𝐜𝐚𝐥𝐞…

Tired of your 1T param language model loss plateauing ~0.6-1.3? Simple solution: cheat by learning a latent language with better characteristics than English! Provocative title aside, I explored whether machines could develop their own "language" optimized for AI vs humans. 🧵

"How Many Instructions Can LLMs Follow at Once?" In this paper they found that leading LLMs can satisfy only about 68% of 500 concurrent instructions, showing a bias toward earlier instructions.