12 citations · 20 across the 10 of their papers we have counts for
11 papers
LitBench: A Benchmark and Dataset for Reliable Evaluation of Creative Writing
Daniel Fein, Sebastian Russo, Violet Xiang +3
Evaluating creative writing generated by large language models (LLMs) remains challenging because open-ended narratives lack ground truths. Without performant automated evaluation…
Towards Embodiment Scaling Laws in Robot Locomotion
Bo Ai, Liu Dai, Nico Bohlinger +7
Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodim…
RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
Charles Xu, Qiyang Li, Jianlan Luo +1
Recent advances in robotic foundation models have enabled the development of generalist policies that can adapt to diverse tasks. While these models show impressive flexibility, th…
Generative Reward Models
Dakota Mahan, Duy Van Phung, Rafael Rafailov +6
Reinforcement Learning from Human Feedback (RLHF) has greatly improved the performance of modern Large Language Models (LLMs). The RLHF process is resource-intensive and technicall…
Collapse or Thrive? Perils and Promises of Synthetic Data in a Self-Generating World
Joshua Kazdan, Rylan Schaeffer, Apratim Dey +4
What happens when generative machine learning models are pretrained on web-scale datasets containing data generated by earlier models? Some prior work warns of "model collapse" as…
Scalable Ensembling For Mitigating Reward Overoptimisation
Ahmed M. Ahmed, Rafael Rafailov, Stepan Sharkov +2
Reinforcement Learning from Human Feedback (RLHF) has enabled significant advancements within language modeling for powerful, instruction-following models. However, the alignment o…
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms
Rafael Rafailov, Yaswanth Chittepu, Ryan Park +5
Reinforcement Learning from Human Feedback (RLHF) has been crucial to the recent success of Large Language Models (LLMs), however, it is often a complex and brittle process. In the…
Offline Regularised Reinforcement Learning for Large Language Models Alignment
Pierre Harvey Richemond, Yunhao Tang, Daniel Guo +15
The dominant framework for alignment of large language models (LLM), whether through reinforcement learning from human feedback or direct preference optimisation, is to learn from…
Efficient Imitation Learning with Conservative World Models
Victor Kolev, Rafael Rafailov, Kyle Hatch +2
We tackle the problem of policy learning from expert demonstrations without a reward function. A central challenge in this space is that these policies fail upon deployment due to…
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey +11
The proliferation of generative models, combined with pretraining on web-scale data, raises a timely question: what happens when these models are trained on their own generated out…
From to : Your Language Model is Secretly a Q-Function
Rafael Rafailov, Joey Hejna, Ryan Park +1
Reinforcement Learning From Human Feedback (RLHF) has been critical to the success of the latest generation of generative AI models. In response to the complex nature of the classi…