10 citations · 11 across the 2 of their papers we have counts for
6 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…