Yang Chen @ychenNLP
Research Scientist @NVIDIA | PhD @GeorgiaTech| RL and LLM reasoning edchengg.github.io Joined September 2018-
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Progress at our datacenter in Abilene. Fun to visit yesterday!
@techdevnotes Just as we will be the first to bring a Gigawatt of coherent training compute online, we will also be the first to 10GW, 100GW, 1TW, …
If intelligence is the log of compute… it starts with a lot of compute! And that’s why we’re scaling our GPU fleet faster than anyone else. Just last year, we added over 2 gigawatts of new capacity – roughly the output of 2 nuclear power plants. And today we’re going further,…
@ns123abc @SemiAnalysis_ @BrentM_SpaceX 1TW is a start
Having thought about it some more, I think the 50 million H100 equivalent number in 5 years is about right. Eventually, billions.
Having thought about it some more, I think the 50 million H100 equivalent number in 5 years is about right. Eventually, billions.
Cable pr0n of @xai GB200 servers at Colossus 2
we have signed a deal for an additional 4.5 gigawatts of capacity with oracle as part of stargate. easy to throw around numbers, but this is a _gigantic_ infrastructure project. some progress photos from abilene:
Our released evaluation toolkit can reproduce our AceReason-Nemotron models numbers (see below): AceReason-Nemotron-1.0-7B: LiveCodeBench (Avg@8): * [05/23-05/24]: 72.0; [06/24-01/25]: 54.2 * release set v5: 51.2; release set v6: 44.4 AIME (Avg@64): * AIME'24: 68.6; AIME'25:…
Our released evaluation toolkit can reproduce our AceReason-Nemotron models numbers (see below): AceReason-Nemotron-1.0-7B: LiveCodeBench (Avg@8): * [05/23-05/24]: 72.0; [06/24-01/25]: 54.2 * release set v5: 51.2; release set v6: 44.4 AIME (Avg@64): * AIME'24: 68.6; AIME'25:…
The first thing we did was to make sure the eval setup is correct! We spend a lot of time to make sure our eval can - accurately reproduce the DeepSeek-R1 numbers on AIME, LiveCodeBench - it's IMPOSSIBLE to track the RL progress without a good eval set up (e.g., we see AIME up…
The first thing we did was to make sure the eval setup is correct! We spend a lot of time to make sure our eval can - accurately reproduce the DeepSeek-R1 numbers on AIME, LiveCodeBench - it's IMPOSSIBLE to track the RL progress without a good eval set up (e.g., we see AIME up…
📌Paper: arxiv.org/abs/2506.13284 📌Model: huggingface.co/nvidia/AceReas… 📌SFT Data: huggingface.co/datasets/nvidi… 📌Math RL Data: huggingface.co/datasets/nvidi… A series of our work on reasoning models: 📌5/22/2025: AceReason-Nemotron: Scaling RL for math and code (7B and 14B)…
With stronger SFT backbone, AceReason-Nemotron-1.1-7B significantly outperforms its predecessor and sets a record-high performance among Qwen2.5-7B-based reasoning models. 📄Report: arxiv.org/pdf/2506.13284 🤗Model: huggingface.co/nvidia/AceReas… 📚SFT Data: huggingface.co/datasets/nvidi…
With stronger SFT backbone, AceReason-Nemotron-1.1-7B significantly outperforms its predecessor and sets a record-high performance among Qwen2.5-7B-based reasoning models. 📄Report: arxiv.org/pdf/2506.13284 🤗Model: huggingface.co/nvidia/AceReas… 📚SFT Data: huggingface.co/datasets/nvidi…
Introducing AceReason-Nemotron 1.1 Our previous release, AceReason-Nemotron-1.0, introduced a stage-wise RL recipe that was applied sequentially to math-only and code-only prompts, demonstrating both high efficiency and strong effectiveness. Here, we systematically investigate…
@etash_guha @ryanmart3n I tried to reproduce DS-R1-distilled-7B and AceReason-7B's performance on your split (06/24-01/25), and they turn out to be 41.9 and 54.6 correspondingly, which is obviously higher than your reported number. Anything wrong here? @etash_guha @ryanmart3n
Does RL incentive reasoning capability over the starting SFT model? We show an interesting result with our recent published AceReason-Nemotron-7B model, which was trained with RL pass@K from 1 to 1024 consistently +10% on LiveCodeBench v6 perhaps scaling RL is the key
Does RL incentive reasoning capability over the starting SFT model? We show an interesting result with our recent published AceReason-Nemotron-7B model, which was trained with RL pass@K from 1 to 1024 consistently +10% on LiveCodeBench v6 perhaps scaling RL is the key
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