4 papers · 1 filter
CascadeFormer: Depth-Tapered Transformers Motivated by Gradient Fan-in Asymmetry
Huzama Ahmad, Cao Viet Hai Nam, Se-Young Yun
Deep Transformers are composed of uniformly stacked residual blocks, yet their deepest layers often add little value. We present two efficiency methods that exploit this asymmetry.…
SpotAttention: Plug-In Block-Sparse Routing for Pretrained Long-Context Transformers
Huzama Ahmad, Se-Young Yun
Long contexts have become standard in pretrained LLMs, yet they remain expensive to run: prefill compute grows quadratically with sequence length, and every decode step re-reads a…
Process-Verified Reinforcement Learning for Theorem Proving via Lean
Minsu Kim, Se-Young Yun
While reinforcement learning from verifiable rewards (RLVR) typically has relied on a single binary verification signal, symbolic proof assistants in formal reasoning offer rich, f…
Provably Efficient Regularized Online RLHF with Generalized Bilinear Preferences
Junghyun Lee, Minju Hong, Kwang-Sung Jun +2
We consider the problem of regularized best-response max-regret minimization in online RLHF under general preferences and bandit feedback. While various regularizers are utilized t…