40 citations · 106 across the 28 of their papers we have counts for
3 papers · 1 filter
TailSFT: Filtered Fine-Tuning Improves Post-Training Performance
Sadhika Malladi, Samy Jelassi, Dylan Foster +2
Reinforcement learning post-training drives reasoning and agentic capabilities in modern AI systems, yet a growing body of work shows that it is most effective when used to fine-tu…
The Recurrent Transformer: Greater Effective Depth and Efficient Decoding
Costin-Andrei Oncescu, Depen Morwani, Samy Jelassi +3
Transformers process tokens in parallel but are temporally shallow: at position , each layer attends to key-value pairs computed based on the previous layer, yielding a depth ca…
Matching Features, Not Tokens: Energy-Based Fine-Tuning of Language Models
Samy Jelassi, Mujin Kwun, Rosie Zhao +5
Cross-entropy (CE) training provides dense and scalable supervision for language models, but it optimizes next-token prediction under teacher forcing rather than sequence-level beh…