3 papers indexed
Loïc Cabannes, Pierre-Emmanuel Mazaré, Gergely Szilvasy, Matthijs Douze, Maria Lomeli, Ilze Amanda Auzina, et al.
Linear attention models allow a fixed state size and a fixed amount of compute per token. However, due to their limited state size, linear attention models fall behind in long-context recall compared to softmax-attention-based transformer architectures. Increasing the state size…
Juliette Decugis, Fabian Gloeckle, Francis Bach, Taco Cohen, Gabriel Synnaeve
How can Large Language Models (LLMs) solve problems they currently cannot? Repeated sampling scales test-time compute but GPU cost grows linearly with attempts, while reinforcement learning (RL) with verifiable rewards improves single-attempt accuracy at the expense of sample div…
Juliette Decugis, Sean O'Brien, Francis Bach, Gabriel Synnaeve, Taco Cohen
Reinforcement learning post-training dramatically improves LLM reasoning, but suffers from training instability and diversity collapse. Advantage functions offer an appealing fix: they reshape the training objective, reweight which rollouts drive learning, and are trivial to impl…