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cs.LG2026
Timestep Weighting: A Hidden Key to Effective ELBO-Based Flow-Matching RL
Qinwei Ma, Jingzhe Shi, Simin Fan +3
ELBO-based reinforcement learning offers a sampler-agnostic approach to fine-tuning flow matching models with reward feedback. Timestep weighting in ELBO-based RL has large impact…
cs.LG2026
The Magic Correlations: Understanding Knowledge Transfer from Pretraining to Supervised Fine-Tuning
Simin Fan, Dimitris Paparas, Natasha Noy +3
Understanding how language model capabilities transfer from pretraining to supervised fine-tuning (SFT) is fundamental to efficient model development and data curation. In this wor…
cs.LG2024
Dynamic Gradient Alignment for Online Data Mixing
Simin Fan, David Grangier, Pierre Ablin
The composition of training data mixtures is critical for effectively training large language models (LLMs), as it directly impacts their performance on downstream tasks. Our goal…