mei @multiply_matrix
training foundation models. AGI timeline 2027. MIT dropout. DMs are open linkedin.com/in/matrixmulti… Joined February 2021-
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all the exits in ai have been acquihires🧐 one top ai researcher is worth more than an entire non-ai company with PMF revenue? 😱 windsurf --> Google character.ai -->Google deepmind --> Google scale.ai --> Meta inflection --> Microsoft adept…
all the exits in ai have been acquihires🧐 one top ai researcher is worth more than an entire non-ai company with PMF revenue? 😱 windsurf --> Google character.ai -->Google deepmind --> Google scale.ai --> Meta inflection --> Microsoft adept…
all the exits in ai have been acquihires🧐 scale.ai --> Meta character.ai --> Google deepmind --> Google inflection --> Microsoft adept --> Amazon mosaic --> Databricks
all the exits in ai have been acquihires🧐 scale.ai --> Meta character.ai --> Google deepmind --> Google inflection --> Microsoft adept --> Amazon mosaic --> Databricks
models primed with INCORRECT solutions but with RIGHT BEHAVIORS achieve identical performance to those trained on correct solutions? > optimize for behaviors & amplify with RL
models primed with INCORRECT solutions but with RIGHT BEHAVIORS achieve identical performance to those trained on correct solutions? > optimize for behaviors & amplify with RL https://t.co/KaxC1TNXp7
9/13 Our findings reveal a fundamental connection between a model's initial reasoning behaviors and its capacity for improvement through RL. Models that explore verification, backtracking, subgoals, and backward chaining are primed for success.
This is the dataset we curated for our own reasoning experiments. There is a lot of reasoning data coming out now, but we spend extra time on this to make sure all the problems are high-quality and suitable for RL training!
This is the dataset we curated for our own reasoning experiments. There is a lot of reasoning data coming out now, but we spend extra time on this to make sure all the problems are high-quality and suitable for RL training!
still climbing 📈 Big-MATH just hit 🥈 on @huggingface huggingface.co/datasets/Synth…
still climbing 📈 Big-MATH just hit 🥈 on @huggingface huggingface.co/datasets/Synth…
Superintelligence isn't about discovering new things; it's about discovering new ways to discover I think our latest work formalizes Meta Chain-of-Thought which we believe lies on the path to ASI When we train models on the problem-solving process itself—rather than the final…
Superintelligence isn't about discovering new things; it's about discovering new ways to discover I think our latest work formalizes Meta Chain-of-Thought which we believe lies on the path to ASI When we train models on the problem-solving process itself—rather than the final…
Despite all the twitter hype there still hasn't been public proof that the "reasoning" models have any emergence. I.e. is there a class of problems that are solvable with "advanced reasoning" that were not under GPT4o with search under some computational budget?
Scaling LLMs with more data is hitting its limits. To address more complex tasks, we need innovative approaches. Shifting from teaching models what to answer to how to solve problems, leveraging test-time compute and meta-RL, could be the solution. Check out Rafael's 🧵 below!
Scaling LLMs with more data is hitting its limits. To address more complex tasks, we need innovative approaches. Shifting from teaching models what to answer to how to solve problems, leveraging test-time compute and meta-RL, could be the solution. Check out Rafael's 🧵 below!
Thrilled to share our latest work on consistency models! We simplified the math behind continuous-time consistency models, stabilized their training, and scaled them up to 1.5B parameters. We are now one step closer to real-time multimodal generation!
Thrilled to share our latest work on consistency models! We simplified the math behind continuous-time consistency models, stabilized their training, and scaled them up to 1.5B parameters. We are now one step closer to real-time multimodal generation!
New follow-up work on the effects of synthetic data on model pre-training. It’s becoming increasingly clear that the model collapse issues predicted by prior works are not panning out in theory and practice. Industry labs now even have entire synthetic data pre-training teams.
New follow-up work on the effects of synthetic data on model pre-training. It’s becoming increasingly clear that the model collapse issues predicted by prior works are not panning out in theory and practice. Industry labs now even have entire synthetic data pre-training teams.
Many (including me) who believed in RL were waiting for a moment when it will start scaling in a general domain similarly to other successful paradigms. That moment finally has arrived and signifies a meaningful increase in our understanding of training neural networks
the brain finetunes on synthetic data while it sleeps biorxiv.org/content/10.110…
whichever lab wins gold in the IMO, please do it in a way that generalizes and isn't just specific to math 🙏
whichever lab wins gold in the IMO, please do it in a way that generalizes and isn't just specific to math 🙏
@cindyxywu Evolution used something less than infinite compute to arrive at intelligence. The question is, just how much can it be accelerated by applying more intelligence to the search process. Possibly the search process is fundamentally compute bound rather than intelligence bound.
@demishassabis @NoamShazeer @GoogleDeepMind @Google Timeline defining event
AGI became inevitable after Claude Shannon, Ray Solomonoff, & Andrey Kolmogorov theorized that compression = intelligence. everything else was simply a matter of time
AGI became inevitable after Claude Shannon, Ray Solomonoff, & Andrey Kolmogorov theorized that compression = intelligence. everything else was simply a matter of time
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13K Followers 433 Following Building next-gen AI at @thinkymachines. Past: Founding team @MistralAI, RS at Facebook AI Research. Ph.D. @SCSatCMU, BTech @iitbombay CS.
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