36 citations · 165 across the 31 of their papers we have counts for
4 papers · 1 filter
Learning to Reason Efficiently with Discounted Reinforcement Learning
Alex Ayoub, Kavosh Asadi, Dale Schuurmans +2
Large reasoning models (LRMs) often consume excessive tokens, inflating computational cost and latency. More broadly, in goal reaching sequential decision problems we often want to…
InstructVTON: Optimal Auto-Masking and Natural-Language-Guided Interactive Style Control for Inpainting-Based Virtual Try-On
Julien Han, Shuwen Qiu, Qi Li +4
We present InstructVTON, an instruction-following interactive virtual try-on system that allows fine-grained and complex styling control of the resulting generation, guided by natu…
C2-DPO: Constrained Controlled Direct Preference Optimization
Kavosh Asadi, Julien Han, Idan Pipano +5
Direct preference optimization (\texttt{DPO}) has emerged as a promising approach for solving the alignment problem in AI. In this paper, we make two counter-intuitive observations…
Adjoint sharding for very long context training of state space models
Xingzi Xu, Amir Tavanaei, Kavosh Asadi +1
Despite very fast progress, efficiently training large language models (LLMs) in very long contexts remains challenging. Existing methods fall back to training LLMs with short cont…