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Provably mitigating overoptimization in rlhf: Your sft loss is implicitly an adversarial regularizer
Z Liu, M Lu, S Zhang, B Liu, H Guo, Y Yang… - ar** or herding? reward model ensembles mitigate but do not eliminate reward hacking
Reward models play a key role in aligning language model applications towards human
preferences. However, this setup creates an incentive for the language model to exploit …
preferences. However, this setup creates an incentive for the language model to exploit …
Regularizing hidden states enables learning generalizable reward model for llms
Reward models trained on human preference data have been proven to effectively align
Large Language Models (LLMs) with human intent within the framework of reinforcement …
Large Language Models (LLMs) with human intent within the framework of reinforcement …
Inverse-rlignment: Inverse reinforcement learning from demonstrations for llm alignment
Aligning Large Language Models (LLMs) is crucial for enhancing their safety and utility.
However, existing methods, primarily based on preference datasets, face challenges such …
However, existing methods, primarily based on preference datasets, face challenges such …
Correcting the mythos of kl-regularization: Direct alignment without overoptimization via chi-squared preference optimization
Language model alignment methods, such as reinforcement learning from human feedback
(RLHF), have led to impressive advances in language model capabilities, but existing …
(RLHF), have led to impressive advances in language model capabilities, but existing …
On Uncertainty In Natural Language Processing
D Ulmer - arxiv preprint arxiv:2410.03446, 2024 - arxiv.org
The last decade in deep learning has brought on increasingly capable systems that are
deployed on a wide variety of applications. In natural language processing, the field has …
deployed on a wide variety of applications. In natural language processing, the field has …
Reusing Embeddings: Reproducible Reward Model Research in Large Language Model Alignment without GPUs
Large Language Models (LLMs) have made substantial strides in structured tasks through
Reinforcement Learning (RL), demonstrating proficiency in mathematical reasoning and …
Reinforcement Learning (RL), demonstrating proficiency in mathematical reasoning and …
Reviving The Classics: Active Reward Modeling in Large Language Model Alignment
Building neural reward models from human preferences is a pivotal component in
reinforcement learning from human feedback (RLHF) and large language model alignment …
reinforcement learning from human feedback (RLHF) and large language model alignment …
Sail into the Headwind: Alignment via Robust Rewards and Dynamic Labels against Reward Hacking
Aligning AI systems with human preferences typically suffers from the infamous reward
hacking problem, where optimization of an imperfect reward model leads to undesired …
hacking problem, where optimization of an imperfect reward model leads to undesired …
The Energy Loss Phenomenon in RLHF: A New Perspective on Mitigating Reward Hacking
This work identifies the Energy Loss Phenomenon in Reinforcement Learning from Human
Feedback (RLHF) and its connection to reward hacking. Specifically, energy loss in the final …
Feedback (RLHF) and its connection to reward hacking. Specifically, energy loss in the final …