Adi Renduchintala
@rendu_a
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Applied Research Scientist @NVIDIA, former: Research Scientist @MetaAI, PhD @jhuclsp also lurking on Mastodon [email protected]
Redwood City, CA
Joined July 2016
We found a new way to get language models to reason. 🤯 No RL, no training, no verifiers, no prompting. ❌ With better sampling, base models can achieve single-shot reasoning on par with (or better than!) GRPO while avoiding its characteristic loss in generation diversity.
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We are excited to release Nvidia-Nemotron-Nano-V2 model! This is a 9B hybrid SSM model with open base model and training data. This model also supports runtime "thinking" budget control. HF collection with base and post trained models: https://t.co/n3M01d8lSm
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We have been hard at work on improving hybrid models! Looking forward to see how mamba hybrid models shape the Reasoning LLM space. I’m Super excited to be a part of this effort.
We're excited to share leaderboard-topping 🏆 NVIDIA Nemotron Nano 2, a groundbreaking 9B parameter open, multilingual reasoning model that's redefining efficiency in AI and earned the leading spot on the @ArtificialAnlys Intelligence Index leaderboard among open models within
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NVIDIA’s Graduate Fellowship Program is now accepting applications for the 2026–2027 academic year. Selected Ph.D. students receive tuition and stipend coverage up to $60K, plus mentorship and technical support from top NVIDIA researchers during an NVIDIA internship. If you’re
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AI model post training is rapidly improving. The plot below (starting from the same base model) illustrates about 10 months of progress in the *open* post-training research. I’m not convinced that closed research can move as fast.
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Transformers are still dominating the LLM scene but we show that higher throughput alternatives exist which are just as strong! Grateful to have a part in Nemotron-H Reasoning effort. 🙏 Technical report will be out soon, stay tuned!
👀 Nemotron-H tackles large-scale reasoning while maintaining speed -- with 4x the throughput of comparable transformer models.⚡ See how #NVIDIAResearch accomplished this using a hybrid Mamba-Transformer architecture, and model fine-tuning ➡️ https://t.co/AuHYANG9gX
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Some people have said that OpenAI achieved state of the art results on the SWE-Bench Verified leaderboard with their codex model, but that's actually not quite correct, no matter how you measure it. A quick 🧵
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NSF budgets slashed by 50%, ongoing grants cancelled, NSF staff drastically reduced, all 37 divisions abolished, and grants will now be reviewed by a political kommissar. How will that help technological leadership? https://t.co/f9b4yOXdZk
linkedin.com
NSF budgets slashed by 50%, ongoing grants cancelled, NSF staff drastically reduced, all 37 divisions abolished, and grants will now be reviewed by a political kommissar. How will that help technol...
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idk dude I come here and look at my feed, literally everyone on my following feed is subscribed to DOGE and not a single professional scientific researcher has noted that every division at the NSF was just abolished
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Markets down 📉 NVIDIA LLM research 📈 Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models! Better or on-par accuracy compared to other similar open-sourced transformer models while being up to ✨ 3× faster at inference ✨ https://t.co/s9MvKYBHEc
arxiv.org
As inference-time scaling becomes critical for enhanced reasoning capabilities, it is increasingly becoming important to build models that are efficient to infer. We introduce Nemotron-H, a family...
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We are excited to release Llama-Nemotron-Ultra! This is a reasoning ON/OFF, dense 253B model. Open weights and post-training data. https://t.co/dCTacylBR8 We started with llama-405B, changed it via NAS pruning then followed by reasoning-focused post-training: SFT + RL in FP8.
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Nemotron-H: A family of Hybrid Mamba-Transformer LLMs. * Hybrid architecture means up to 3X faster at the same accuracy * Trained in FP8 * Great for VLMs * Weights and instruct versions to come soon. https://t.co/h3dLuDuiUz
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This paper provides some really interesting insights: 1. Previously, people found that Qwen base models are particularly good at R1 training to show strong exploration skills. - This paper shows that there is no magic about Qwen base models. It's likely pre-trained with
🪂Understanding R1-Zero-Like Training: A Critical Perspective * DeepSeek-V3-Base already exhibits "Aha moment" before RL-tuning?? * The ever-increasing output length in RL-tuning might be due to a BIAS in GRPO?? * Getting GRPO Done Right, we achieve a 7B AIME sota! 🧵 📜Full
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We are excited to release new Llama-Nemotron models. These models allow you to set reasoning ON/OFF during runtime. We also release all the post-training data under CC-BY-4! Try it now on https://t.co/Q7jFIMi0po HF collection: https://t.co/E8mNZlUXz0
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DPO, SimPO, RPO, ... There are just too many **PO**s in the NLP/LLM world! 💥💥💥😲 If you wonder which PO truly works the best, how to make them even better, and their inter-connections, read our latest paper at https://t.co/n5i63aoqha 👇 (1/3)
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Our team put together a unified mathematical framework to analyze popular model alignment algorithms. “Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment” https://t.co/qz99EZGJx0
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So collusion rings are still a thing at top conferences. @openreviewnet has all the data across all top tier ml conferences — what are they currently do to solve this. What top tier conf is willing to release data of bidding patterns so we can analyze the problem at scale.
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🤔 Pre-training as Ilya knows it will end, but not for us. At NeurIPS, @ilyasut shared an insightful perspective: "pre-training as we know it will end". I fully agree that agents, synthetic data, and inference-time computing are critical breakthroughs for the superintelligence,
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🥳Once again I am looking for PhD students in 💬persuasion, 💬dialogues and ⛑️AI safety, to join our CHATS lab @Northeastern in Fall 2025! 🥳 Apply by 12/15 both in ECE and CS! Let’s build and break chatbots together🥳! https://t.co/uXf2P1FKSG
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