
Melissa Hall
120 posts

Melissa Hall
@hall__melissa
Working on fairness & responsibility in AI @FacebookAI Research. Previously studying disinfo @AtlanticCouncil's @DFRLab.


Reward models make or break post-training for multimodal omni models (e.g., nano banana), yet there’s surprisingly little research on that‼️ We’re releasing MMRB2: new reward benchmark focusing on omni models, spanning T2I, editing, interleaved, and thinking with images 🧵1/n


"Increasing the Utility of Synthetic Images through Chamfer Guidance" has been accepted at NeurIPS 2025! 🎉 We're happy to share our approach for generating synthetic images that are both diverse and high-quality. #NeurIPS2025 Paper: arxiv.org/abs/2508.10631







Join us Thursday afternoon in Room 106A for a great set of speakers, panelists, and contributed works focused on Responsible Generative AI @CVPR!! #CVPR2025




🚀 New Paper Alert! Can we generate informative synthetic data that truly helps a downstream learner? Introducing Deliberate Practice for Synthetic Data (DP)—a dynamic framework that focuses on where the model struggles most to generate useful synthetic training examples. 🔥 On ImageNet-1k, DP reduces dataset size by 55 million examples while outperforming prior synthetic benchmarks! 📄Paper: arxiv.org/pdf/2502.15588 🧵Key takeaways ⬇️

BLT model weights are out! Responding to popular demand, we just open-sourced model weights for our 1B and 8B BLT models for the research community to play with! huggingface.co/facebook/blt Hoping to see many new and improved BLT based architectures this year!






🚀 New Paper Alert! Can we generate informative synthetic data that truly helps a downstream learner? Introducing Deliberate Practice for Synthetic Data (DP)—a dynamic framework that focuses on where the model struggles most to generate useful synthetic training examples. 🔥 On ImageNet-1k, DP reduces dataset size by 55 million examples while outperforming prior synthetic benchmarks! 📄Paper: arxiv.org/pdf/2502.15588 🧵Key takeaways ⬇️



