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20182024
most citedGuiding Pretraining in Reinforcement Learning with Large Language Models

39 citations · 131 across the 11 of their papers we have counts for

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8 papers · 1 filter

cs.LG2024★ 5 cited

A StrongREJECT for Empty Jailbreaks

Alexandra Souly, Qingyuan Lu, Dillon Bowen +8

Most jailbreak papers claim the jailbreaks they propose are highly effective, often boasting near-100% attack success rates. However, it is perhaps more common than not for jailbre…

cs.LG2023★ 8 cited

Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game

Sam Toyer, Olivia Watkins, Ethan Adrian Mendes +9

While Large Language Models (LLMs) are increasingly being used in real-world applications, they remain vulnerable to prompt injection attacks: malicious third party prompts that su…

cs.LG2023★ 18 cited

DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models

Ying Fan, Olivia Watkins, Yuqing Du +7

Learning from human feedback has been shown to improve text-to-image models. These techniques first learn a reward function that captures what humans care about in the task and the…

cs.LG2023★ 35 cited

Aligning Text-to-Image Models using Human Feedback

Kimin Lee, Hao Liu, Moonkyung Ryu +6

Deep generative models have shown impressive results in text-to-image synthesis. However, current text-to-image models often generate images that are inadequately aligned with text…

cs.LG2023★ 39 cited

Guiding Pretraining in Reinforcement Learning with Large Language Models

Yuqing Du, Olivia Watkins, Zihan Wang +5

Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped reward function. Intrinsically motivated exploration methods address this limitation by…

cs.LG2022

Teachable Reinforcement Learning via Advice Distillation

Olivia Watkins, Trevor Darrell, Pieter Abbeel +2

Training automated agents to complete complex tasks in interactive environments is challenging: reinforcement learning requires careful hand-engineering of reward functions, imitat…

cs.LG2022★ 3 cited

Explaining Reinforcement Learning Policies through Counterfactual Trajectories

Julius Frost, Olivia Watkins, Eric Weiner +4

In order for humans to confidently decide where to employ RL agents for real-world tasks, a human developer must validate that the agent will perform well at test-time. Some policy…

cs.LG2018★ 8 cited

Program Language Translation Using a Grammar-Driven Tree-to-Tree Model

Mehdi Drissi, Olivia Watkins, Aditya Khant +5

The task of translating between programming languages differs from the challenge of translating natural languages in that programming languages are designed with a far more rigid s…