Aditi Mavalankar

140 posts

Aditi Mavalankar

Aditi Mavalankar

@aditimavalankar

Research Scientist @DeepMind

London, UK Katılım Mart 2017
420 Takip Edilen2K Takipçiler
Aditi Mavalankar retweetledi
Abhinav Moudgil
Abhinav Moudgil@amoudgl·
Introducing Celo2: Towards Learned Optimization Free Lunch We show that learned optimizers can generalize to practical tasks like GPT-3 1.3B pretraining and several out-of-distribution vision/RL tasks from limited meta-training (~4.5 GPU hours)! 🧵
Abhinav Moudgil tweet media
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Seijin Kobayashi
Seijin Kobayashi@SeijinKobayashi·
Standard reinforcement learning in raw tokens is a disaster for sparse rewards! Here, we propose 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗥𝗟: acting on abstract actions emerging in the residual stream representation. A paradigm shift in using pretrained models to solve hard, long-horizon tasks! 🧵
GIF
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Sushant Sachdeva
Sushant Sachdeva@sushnt·
I believe we're at the doorstep of a revolution in how we'll do theoretical research and this is just the beginning! A good time to announce that I've taken leave from UofT to be a part of the revolution on the inside at @OpenAI! :)
Sebastien Bubeck@SebastienBubeck

3 years ago we could showcase AI's frontier w. a unicorn drawing. Today we do so w. AI outputs touching the scientific frontier: cdn.openai.com/pdf/4a25f921-e… Use the doc to judge for yourself the status of AI-aided science acceleration, and hopefully be inspired by a couple examples!

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Google DeepMind
Google DeepMind@GoogleDeepMind·
This is Gemini 3: our most intelligent model that helps you learn, build and plan anything. It comes with state-of-the-art reasoning capabilities, world-leading multimodal understanding, and enables new agentic coding experiences. 🧵
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Luisa Zintgraf
Luisa Zintgraf@luisa_zintgraf·
Excited to share our new paper, "DataRater: Meta-Learned Dataset Curation"! We explore a fundamental question: How can we *automatically* learn which data is most valuable for training foundation models? Paper: arxiv.org/pdf/2505.17895 to appear @NeurIPSConf Thread 👇
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Dan A. Calian
Dan A. Calian@dancalian·
Dataset curation for language models has long relied on brittle, hand-crafted rules. It's time for a more principled, automated approach. Enter DataRater: a meta-learning framework that learns to value data based on downstream training efficiency. Great summary by Luisa below 👇
Luisa Zintgraf@luisa_zintgraf

Excited to share our new paper, "DataRater: Meta-Learned Dataset Curation"! We explore a fundamental question: How can we *automatically* learn which data is most valuable for training foundation models? Paper: arxiv.org/pdf/2505.17895 to appear @NeurIPSConf Thread 👇

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Aditi Mavalankar
Aditi Mavalankar@aditimavalankar·
Gemini with advanced deep think achieved gold medal-level performance at IMO 2025!🥇 Very happy to have been a small part of this collaboration on the inference side, and congrats to everyone involved!
Google DeepMind@GoogleDeepMind

An advanced version of Gemini with Deep Think has officially achieved gold medal-level performance at the International Mathematical Olympiad. 🥇 It solved 5️⃣ out of 6️⃣ exceptionally difficult problems, involving algebra, combinatorics, geometry and number theory. Here’s how 🧵

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Aditi Mavalankar
Aditi Mavalankar@aditimavalankar·
This was a really fun collaboration with my brilliant collaborators Hassan Mansoor, Zita Marinho, Masha Samsikova, and Tom Schaul!
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Aditi Mavalankar
Aditi Mavalankar@aditimavalankar·
In addition to this, AuPair has been shown to work better across codeforces difficulty levels and preserve coverage of problem categories from the training data distribution (see paper for more details).
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Aditi Mavalankar
Aditi Mavalankar@aditimavalankar·
Excited to share our recent work, AuPair, an inference-time technique that builds on the premise of in-context learning to improve LLM coding performance! arxiv.org/abs/2502.18487
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