Active Research

Reinforcement Learning with Human Feedback as a Knowledge Distillation Tool

This project targets the algorithms behind the massively successful applications of Reinforcement Learning with Human Feedback, which is the training process behind ChatGPT, LLaMA II, and plenty other instruction-tuned language models. Specifically, Proximal Policy Optimization provides a routine for training a policy model to optimize a scalar reward. As with many other domain applications in RL, without proper guard rails the model will simply hack this reward, and generate outputs which have very high reward values but do not maintain the desired semantic and lexical integrity.

One aspect of modern PPO implementations which addresses this is the creation of a "reference model", a copy of the policy model created before any RL training occurs. Then, each token-wise generation of the policy model is compared to the reference model and an "active-token" Kullback-Leibler divergence approximation is computed. The policy model is tasked with minimizing this divergence while maximizing the scalar reward, creating a less-hackable optimization landscape. This can be formulated as follows (source):

The original research question for this project was "How do we incorporate stimuli from API-restricted large language models which we do not have access to the parameters of?" and our focus quickly landed on utilizing these reference models to do so. While testing the use of various reference models, it becomes quickly apparent that there is some type of traditional knowledge distillation occuring in this RL setting. We then have turned our investigation toward understanding and describing this knowledge distillation which is more apparent when a different sized reference model is utilized.

The current research repository is built off of Allen-AI's RL4LMs modular repository and can be found here. This project is under advisement by Dr. Kartik Goyal and the computational resources in which our experiments are conducted on are supplied by Toyota Technical Institute of Technology. For collaboration or inquiries, please feel free to contact me via email.

Thesis Research Project

Exploring Denoising Autoencoder Architectures in Self-Supervised Learning

This project proposed a novel self-supervised learning algorithm as well as exploring the effect of reconstruction and embedding regularization in autoencoders. The novel algorithm involved using a denoising autoencoder to create representations that were then used for downstream tasks like image classification. Utilizing denoising autoencoders as an alternative to successful contrastive methods like SimCLR and Barlow Twins is desirable because of the lack of dependence between batch size and downstream task performance. It is well known that Contrastive methods require extremely large batch sizes to form "negative" image augmentation pairs and be effective. In many scenarios, this can be computationally prohibitive and otherwise undesirable. Although our novel method does not defeat the goliath that is SimCLR in standard circumstances, we illustrate that there are certain situations such as in high augmentation strength and low training time regimes that our method outperforms SimCLR. We also explore the effect of the denoising reconstruction loss, and posit that this loss forces the model to become robust to augmentations and thus form an efficient representation space. This project was completed under the advisement of Dr. Yali Amit of the University of Chicago Statistics Department.