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Jelena Luketina

4 accepted papers

2024

Understanding the Effects of RLHF on LLM Generalisation and Diversity

ICLR 2024poster

Large language models (LLMs) fine-tuned with reinforcement learning from human feedback (RLHF) have been used in some of the most widely deployed AI models to date, such as OpenAI's ChatGPT or Anthropic's Claude. While there has been significant work developing these methods, our understanding of th…

2021

Transient Non-stationarity and Generalisation in Deep Reinforcement Learning

ICLR 2021poster

Non-stationarity can arise in Reinforcement Learning (RL) even in stationary environments. For example, most RL algorithms collect new data throughout training, using a non-stationary behaviour policy. Due to the transience of this non-stationarity, it is often not explicitly addressed in deep RL an…

Cited by 104SourcePDFScholar
2018

Progress & Compress: A scalable framework for continual learning

ICML 2018oral

We introduce a conceptually simple and scalable framework for continual learning domains where tasks are learned sequentially. Our method is constant in the number of parameters and is designed to preserve performance on previously encountered tasks while accelerating learning progress on subsequent…

Cited by 1080SourcePDFScholar
2016

Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters

ICML 2016poster

Hyperparameter selection generally relies on running multiple full training trials, with selection based on validation set performance. We propose a gradient-based approach for locally adjusting hyperparameters during training of the model. Hyperparameters are adjusted so as to make the model parame…