6 papers
Revisiting Group Relative Policy Optimization: Insights into On-Policy and Off-Policy Training
Youssef Mroueh, Nicolas Dupuis, Brian Belgodere +6
We revisit Group Relative Policy Optimization (GRPO) in both on-policy and off-policy optimization regimes. Our motivation comes from recent work on off-policy Proximal Policy Opti…
GP-MoLFormer: A Foundation Model For Molecular Generation
Jerret Ross, Brian Belgodere, Samuel C. Hoffman +4
Transformer-based models trained on large and general purpose datasets consisting of molecular strings have recently emerged as a powerful tool for successfully modeling various st…
Distributional Preference Alignment of LLMs via Optimal Transport
Igor Melnyk, Youssef Mroueh, Brian Belgodere +6
Current LLM alignment techniques use pairwise human preferences at a sample level, and as such, they do not imply an alignment on the distributional level. We propose in this paper…
Risk Aware Benchmarking of Large Language Models
Apoorva Nitsure, Youssef Mroueh, Mattia Rigotti +6
We propose a distributional framework for benchmarking socio-technical risks of foundation models with quantified statistical significance. Our approach hinges on a new statistical…
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs
Brian Belgodere, Pierre Dognin, Adam Ivankay +11
Real-world data often exhibits bias, imbalance, and privacy risks. Synthetic datasets have emerged to address these issues. This paradigm relies on generative AI models to generate…
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…