15 citations · 22 across the 12 of their papers we have counts for
18 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…
Let's (not) just put things in Context: Test-Time Training for Long-Context LLMs
Rachit Bansal, Aston Zhang, Rishabh Tiwari +8
Progress on training and architecture strategies has enabled LLMs with millions of tokens in context length. However, empirical evidence suggests that such long-context LLMs can co…
Let Me Think! A Long Chain-of-Thought Can Be Worth Exponentially Many Short Ones
Parsa Mirtaheri, Ezra Edelman, Samy Jelassi +2
Inference-time computation has emerged as a promising scaling axis for improving large language model reasoning. However, despite yielding impressive performance, the optimal alloc…
Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining
Rosie Zhao, Alexandru Meterez, Sham Kakade +3
Reinforcement learning (RL)-based fine-tuning has become a crucial step in post-training language models for advanced mathematical reasoning and coding. Following the success of fr…