10 citations · 13 across the 7 of their papers we have counts for
7 papers
Teaching LLMs String Matching, Backtracking, and Error Recovery to Deduce Bases and Truth Tables for the Combinatorially Exploding Bit Manipulation Puzzles
Prateek Agnihotri, Sanchit Jain, Prabhat Agnihotri +2
This paper presents our algorithmic innovations for the NVIDIA Nemotron Model Reasoning Challenge, focusing on Bit Manipulation Puzzles. In this task, the objective is to discover…
CodeAlchemy: Synthetic Code Rewriting at Scale
Ankit Gupta, Aditya Prasad, Rameswar Panda
Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats. While synthetic data has proven transformative for language models, code rem…
Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler
Yikang Shen, Matthew Stallone, Mayank Mishra +6
Finding the optimal learning rate for language model pretraining is a challenging task. This is not only because there is a complicated correlation between learning rate, batch siz…
Scaling Granite Code Models to 128K Context
Matt Stallone, Vaibhav Saxena, Leonid Karlinsky +19
This paper introduces long-context Granite code models that support effective context windows of up to 128K tokens. Our solution for scaling context length of Granite 3B/8B code mo…
The infrastructure powering IBM's Gen AI model development
Talia Gershon, Seetharami Seelam, Brian Belgodere +143
AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model…
Granite Code Models: A Family of Open Foundation Models for Code Intelligence
Mayank Mishra, Matt Stallone, Gaoyuan Zhang +43
Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environmen…
Rapid Development of Compositional AI
Lee Martie, Jessie Rosenberg, Veronique Demers +12
Compositional AI systems, which combine multiple artificial intelligence components together with other application components to solve a larger problem, have no known pattern of d…