4 citations · 4 across the 11 of their papers we have counts for
5 papers · 1 filter
PrAg-PO: Prompt Augmented Policy Optimization for Robust and Diverse Mathematical Reasoning
Wenquan Lu, Hai Huang, Enqi Liu +1
Reinforcement learning algorithms such as group-relative policy optimization (GRPO) have shown strong potential for improving the mathematical reasoning capabilities of large langu…
Your Attention Matters: to Improve Model Robustness to Noise and Spurious Correlations
Camilo Tamayo-Rousseau, Yunjia Zhao, Yiqun Zhang +1
Self-attention mechanisms are foundational to Transformer architectures, supporting their impressive success in a wide range of tasks. While there are many self-attention variants,…
Task Priors: Enhancing Model Evaluation by Considering the Entire Space of Downstream Tasks
Niket Patel, Randall Balestriero
The grand goal of AI research, and particularly Self Supervised Learning (SSL), is to produce systems that can successfully solve any possible task. In contrast, current evaluation…
LoRA Users Beware: A Few Spurious Tokens Can Manipulate Your Finetuned Model
Marcel Mateos Salles, Praney Goyal, Pradyut Sekhsaria +2
Large Language Models (LLMs) are commonly finetuned for a variety of use cases and domains. A common approach is to leverage Low-Rank Adaptation (LoRA) -- known to provide strong p…
SplInterp: Improving our Understanding and Training of Sparse Autoencoders
Jeremy Budd, Javier Ideami, Benjamin Macdowall Rynne +2
Sparse autoencoders (SAEs) have received considerable recent attention as tools for mechanistic interpretability, showing success at extracting interpretable features even from ver…