3 papers
cs.LG2026
ReasonCACHE: Teaching LLMs To Reason Without Weight Updates
Sharut Gupta, Phillip Isola, Stefanie Jegelka +4
Can Large language models (LLMs) learn to reason without any weight update and only through in-context learning (ICL)? ICL is strikingly sample-efficient, often learning from only…
cs.LG2025
Learning Diffusion Models with Flexible Representation Guidance
Chenyu Wang, Cai Zhou, Sharut Gupta +4
Diffusion models can be improved with additional guidance towards more effective representations of input. Indeed, prior empirical work has already shown that aligning internal rep…
cs.LG2024
Understanding the Role of Equivariance in Self-supervised Learning
Yifei Wang, Kaiwen Hu, Sharut Gupta +3
Contrastive learning has been a leading paradigm for self-supervised learning, but it is widely observed that it comes at the price of sacrificing useful features (\eg colors) by b…