2 citations · 6 across the 23 of their papers we have counts for
23 papers
Shortcutting the Fix: Identifying and Categorizing Agentic Exploits in Software Engineering Benchmarks
Nikolai Ludwig, Wasi Uddin Ahmad, Somshubra Majumdar +1
High software engineering benchmark scores often mask exploitative shortcuts, such as retrieving upstream solutions, exploiting local Git histories, or recalling memorized code. We…
Post-Training Language Models for Gold-Medal Performance in Coding Competitions
Aleksander Ficek, Sean Narenthiran, Mehrzad Samadi +2
Competitive programming has become a key test of large language model reasoning, with international competitions such as IOI and ICPC representing its most challenging settings. We…
Open-SWE-Traces: Advancing Dual-Mode Multilingual Distillation for Software Engineering Agents
Wasi Uddin Ahmad, Nikolai Ludwig, Somshubra Majumdar +1
The path toward autonomous software engineering is currently bottlenecked by a severe deficit of diverse, large-scale trajectory data. We address this by introducing \ourdataset, a…
Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aaron Blakeman +572
We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 t…
SHERLOC: Structured Diagnostic Localization for Code Repair Agents
Hovhannes Tamoyan, Sean Narenthiran, Erik Arakelyan +2
LLM agents solve repository-level coding tasks through multi-turn tool use, but utilize half their budget on locating faults before editing. Dedicated localization frameworks have…
Workspace Optimization: How to Train Your Agent
Elad Sarafian, Gal Kaplun, Ron Banner +2
Modern agents built on frontier language models often cannot adapt their weights. What, then, remains trainable? We argue it is the agent's \emph{workspace}, the structured externa…
Retrieval from Within: An Intrinsic Capability of Attention-Based Models
Elad Hoffer, Yochai Blau, Edan Kinderman +3
Retrieval-augmented generation (RAG) typically treats retrieval and generation as separate systems. We ask whether an attention-based encoder-decoder can instead retrieve directly…
From SWE-ZERO to SWE-HERO: Execution-free to Execution-based Fine-tuning for Software Engineering Agents
Nikolai Ludwig, Wasi Uddin Ahmad, Somshubra Majumdar +1
We introduce SWE-ZERO to SWE-HERO, a two-stage SFT recipe that achieves state-of-the-art results on SWE-bench by distilling open-weight frontier LLMs. Our pipeline replaces resourc…
Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aakshita Chandiramani +544
We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. Nemo…
NVIDIA Nemotron 3: Efficient and Open Intelligence
NVIDIA, :, Aaron Blakeman +356
We introduce the Nemotron 3 family of models - Nano, Super, and Ultra. These models deliver strong agentic, reasoning, and conversational capabilities. The Nemotron 3 family uses a…
Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aaron Blakeman +311
We present Nemotron 3 Nano 30B-A3B, a Mixture-of-Experts hybrid Mamba-Transformer language model. Nemotron 3 Nano was pretrained on 25 trillion text tokens, including more than 3 t…
Scaling Test-Time Compute to Achieve IOI Gold Medal with Open-Weight Models
Mehrzad Samadi, Aleksander Ficek, Sean Narenthiran +5
Competitive programming has become a rigorous benchmark for evaluating the reasoning and problem-solving capabilities of large language models (LLMs). The International Olympiad in…
NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model
NVIDIA, :, Aarti Basant +214
We introduce Nemotron-Nano-9B-v2, a hybrid Mamba-Transformer language model designed to increase throughput for reasoning workloads while achieving state-of-the-art accuracy compar…
The Challenge of Teaching Reasoning to LLMs Without RL or Distillation
Wei Du, Branislav Kisacanin, George Armstrong +22
Reasoning-capable language models achieve state-of-the-art performance in diverse complex tasks by generating long, explicit Chain-of-Thought (CoT) traces. While recent works show…
OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique
Wasi Uddin Ahmad, Somshubra Majumdar, Aleksander Ficek +6
Recent advancements in reasoning-based Large Language Models (LLMs), particularly their potential through test-time scaling, have created significant opportunities for distillation…
From Output to Evaluation: Does Raw Instruction-Tuned Code LLMs Output Suffice for Fill-in-the-Middle Code Generation?
Wasi Uddin Ahmad, Somshubra Majumdar, Boris Ginsburg
Post-processing is crucial for the automatic evaluation of LLMs in fill-in-the-middle (FIM) code generation due to the frequent presence of extraneous code in raw outputs. This ext…
Llama-Nemotron: Efficient Reasoning Models
Akhiad Bercovich, Itay Levy, Izik Golan +132
We introduce the Llama-Nemotron series of models, an open family of heterogeneous reasoning models that deliver exceptional reasoning capabilities, inference efficiency, and an ope…
SWAN-GPT: An Efficient and Scalable Approach for Long-Context Language Modeling
Krishna C. Puvvada, Faisal Ladhak, Santiago Akle Serrano +8
We present a decoder-only Transformer architecture that robustly generalizes to sequence lengths substantially longer than those seen during training. Our model, SWAN-GPT, interlea…
OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs
Wasi Uddin Ahmad, Aleksander Ficek, Mehrzad Samadi +4
Large Language Models (LLMs) have transformed software development by enabling code generation, automated debugging, and complex reasoning. However, their continued advancement is…
Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models
NVIDIA, :, Aaron Blakeman +198
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 Nemo…
OpenCodeReasoning: Advancing Data Distillation for Competitive Coding
Wasi Uddin Ahmad, Sean Narenthiran, Somshubra Majumdar +5
Since the advent of reasoning-based large language models, many have found great success from distilling reasoning capabilities into student models. Such techniques have significan…
Scoring Verifiers: Evaluating Synthetic Verification for Code and Reasoning
Aleksander Ficek, Somshubra Majumdar, Vahid Noroozi +1
Synthetic verification techniques such as generating test cases and reward modelling are common ways to enhance the coding capabilities of large language models (LLM) beyond predef…
Star Attention: Efficient LLM Inference over Long Sequences
Shantanu Acharya, Fei Jia, Boris Ginsburg
Inference with Transformer-based Large Language Models (LLMs) on long sequences is both costly and slow due to the quadratic complexity of the self-attention mechanism. We introduc…