Jonathan Lai
@_JLai
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Post training @GoogleDeepMind, Gemini Reasoning, training algorithms, RL, opinions are my own
Joined November 2012
Excited to share that I'll be hosting some of the world's best AI researchers and engineers for our @GoogleDeepMind Gemini event next week in Singapore 🇸🇬! Join @JeffDean, @quocleix, @benoitschilling, @melvinjohnsonp and @denny_zhou for a day of technical conversations, panels
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Huge congrats @prateeky2806 and all!! 🎉 It was great to work with everyone here!
Excited to share that our paper on model merging at scale has been accepted to Transactions on Machine Learning Research (TMLR). Huge congrats to my intern @prateeky2806 and our awesome co-authors @_JLai, @alexandraxron, @manaalfar, @mohitban47, and @TsendeeMTS 🎉!!
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Excited to share that our paper on model merging at scale has been accepted to Transactions on Machine Learning Research (TMLR). Huge congrats to my intern @prateeky2806 and our awesome co-authors @_JLai, @alexandraxron, @manaalfar, @mohitban47, and @TsendeeMTS 🎉!!
Ever wondered if model merging works at scale? Maybe the benefits wear off for bigger models? Maybe you considered using model merging for post-training of your large model but not sure if it generalizes well? cc: @GoogleAI @GoogleDeepMind @uncnlp 🧵👇 Excited to announce my
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🚨 New paper 🚨 Excited to share my first paper w/ my PhD students!! We find that advanced LLM capabilities conferred by instruction or alignment tuning (e.g., SFT, RLHF, DPO, GRPO) can be encoded into model diff vectors (à la task vectors) and transferred across model
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Gemini-ийн шинэ загварыг туршаад үзээрэй. Код бичих дээр нилээн сайжирсан байгаа.
Think you know Gemini? 🤔 Think again. Meet Gemini 2.5: our most intelligent model 💡 The first release is Pro Experimental, which is state-of-the-art across many benchmarks - meaning it can handle complex problems and give more accurate responses. Try it now →
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A historic elo margin on LMSYS and also crushed almost all reasoning and STEM benchmarks!! So proud of this team!!
BREAKING: Gemini 2.5 Pro is now #1 on the Arena leaderboard - the largest score jump ever (+40 pts vs Grok-3/GPT-4.5)! 🏆 Tested under codename "nebula"🌌, Gemini 2.5 Pro ranked #1🥇 across ALL categories and UNIQUELY #1 in Math, Creative Writing, Instruction Following, Longer
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1/ Gemini 2.5 is here, and it’s our most intelligent AI model ever. Our first 2.5 model, Gemini 2.5 Pro Experimental is a state-of-the-art thinking model, leading in a wide range of benchmarks – with impressive improvements in enhanced reasoning and coding and now #1 on
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We are rolling out a new Gemini 2.0 Flash Thinking update: - Exp-01-21 variant in AI Studio and API for free - 1 million token context window - Native code execution support - Longer output token generation - Less frequent model contradictions Try it
aistudio.google.com
The fastest path from prompt to production with Gemini
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Try our new experimental thinking model at https://t.co/jcF922lj4j !! Appreciate any and all feedback
Introducing Gemini 2.0 Flash Thinking, an experimental model that explicitly shows its thoughts. Built on 2.0 Flash’s speed and performance, this model is trained to use thoughts to strengthen its reasoning. And we see promising results when we increase inference time
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Prateek is an amazing researcher!! Definitely hire him!
I'm on the job market! Please reach out if you are looking to hire someone to work on - RLHF - Efficiency - MoE/Modular models - Synthetic Data - Test time compute - other phases of pre/post-training. If you are not hiring then I would appreciate a retweet! More details👇
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I'm on the job market! Please reach out if you are looking to hire someone to work on - RLHF - Efficiency - MoE/Modular models - Synthetic Data - Test time compute - other phases of pre/post-training. If you are not hiring then I would appreciate a retweet! More details👇
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🚨✨ Thrilled to share the first study on model merging at large scales by our intern @prateeky2806 @GoogleAI @GoogleDeepMind For larger LLMs merging is an efficient alternative to multitask learning, that can preserve the majority of in-domain performance, while significantly
Ever wondered if model merging works at scale? Maybe the benefits wear off for bigger models? Maybe you considered using model merging for post-training of your large model but not sure if it generalizes well? cc: @GoogleAI @GoogleDeepMind @uncnlp 🧵👇 Excited to announce my
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