7 papers · 1 filter
Normalized Rewards for Preference Optimization
Shawn Im, Federico Danieli, Skyler Seto +2
Direct Alignment Algorithms (DAAs) such as DPO have become a common way to post-train and align LLMs with human preferences. However, DAAs have been observed to over-optimize their…
Cyclical Entropy Eruption: Entropy Dynamics in Agent Reinforcement Learning
Wendi Li, Shawn Im, Sharon Li
Agentic large language models are increasingly used to solve real-world tasks by reasoning over goals, invoking tools, and interacting with external environments. Reinforcement lea…
How Well Can Preference Optimization Generalize Under Noisy Feedback?
Shawn Im, Sharon Li
As large language models (LLMs) advance their capabilities, aligning these models with human preferences has become crucial. Preference optimization, which trains models to disting…
Towards Interpretability Without Sacrifice: Faithful Dense Layer Decomposition with Mixture of Decoders
James Oldfield, Shawn Im, Sharon Li +3
Multilayer perceptrons (MLPs) are an integral part of large language models, yet their dense representations render them difficult to understand, edit, and steer. Recent methods le…
A Unified Understanding and Evaluation of Steering Methods
Shawn Im, Sharon Li
Latent space steering methods provide a practical approach to controlling large language models by applying steering vectors to intermediate activations, guiding outputs toward des…
Can DPO Learn Diverse Human Values? A Theoretical Scaling Law
Shawn Im, Sharon Li
Large language models (LLMs) have demonstrated remarkable capabilities but often struggle to align with human preferences, leading to harmful or undesirable outputs. Preference lea…