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Collections including paper arxiv:2406.11827

Reinforcement Learning (RL / RLHF)

RLHF Workflow: From Reward Modeling to Online RLHF

Paper • 2405.07863 • Published May 13, 2024 • 71
Understanding and Diagnosing Deep Reinforcement Learning

Paper • 2406.16979 • Published Jun 23, 2024 • 10
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4, 2024 • 62
Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning

Paper • 2407.00617 • Published Jun 30, 2024 • 7

mDPO: Conditional Preference Optimization for Multimodal Large Language Models

Paper • 2406.11839 • Published Jun 17, 2024 • 40
Pandora: Towards General World Model with Natural Language Actions and Video States

Paper • 2406.09455 • Published Jun 12, 2024 • 16
WPO: Enhancing RLHF with Weighted Preference Optimization

Paper • 2406.11827 • Published Jun 17, 2024 • 17
In-Context Editing: Learning Knowledge from Self-Induced Distributions

Paper • 2406.11194 • Published Jun 17, 2024 • 20

Diffusion World Model

Paper • 2402.03570 • Published Feb 5, 2024 • 8
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF

Paper • 2401.16335 • Published Jan 29, 2024 • 1
Towards Efficient and Exact Optimization of Language Model Alignment

Paper • 2402.00856 • Published Feb 1, 2024 • 1
ODIN: Disentangled Reward Mitigates Hacking in RLHF

Paper • 2402.07319 • Published Feb 11, 2024 • 14

Bootstrapping Language Models with DPO Implicit Rewards

Paper • 2406.09760 • Published Jun 14, 2024 • 41
BPO: Supercharging Online Preference Learning by Adhering to the Proximity of Behavior LLM

Paper • 2406.12168 • Published Jun 18, 2024 • 7
WPO: Enhancing RLHF with Weighted Preference Optimization

Paper • 2406.11827 • Published Jun 17, 2024 • 17
Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs

Paper • 2406.18629 • Published Jun 26, 2024 • 42

WPO: Enhancing RLHF with Weighted Preference Optimization

Paper • 2406.11827 • Published Jun 17, 2024 • 17
Self-Improving Robust Preference Optimization

Paper • 2406.01660 • Published Jun 3, 2024 • 20
Bootstrapping Language Models with DPO Implicit Rewards

Paper • 2406.09760 • Published Jun 14, 2024 • 41
BPO: Supercharging Online Preference Learning by Adhering to the Proximity of Behavior LLM

Paper • 2406.12168 • Published Jun 18, 2024 • 7

Moral Foundations of Large Language Models

Paper • 2310.15337 • Published Oct 23, 2023 • 1
Specific versus General Principles for Constitutional AI

Paper • 2310.13798 • Published Oct 20, 2023 • 3
Contrastive Prefence Learning: Learning from Human Feedback without RL

Paper • 2310.13639 • Published Oct 20, 2023 • 25
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Paper • 2309.00267 • Published Sep 1, 2023 • 53

Reinforcement Learning (RL / RLHF)

RLHF Workflow: From Reward Modeling to Online RLHF

Paper • 2405.07863 • Published May 13, 2024 • 71
Understanding and Diagnosing Deep Reinforcement Learning

Paper • 2406.16979 • Published Jun 23, 2024 • 10
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4, 2024 • 62
Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning

Paper • 2407.00617 • Published Jun 30, 2024 • 7

Bootstrapping Language Models with DPO Implicit Rewards

Paper • 2406.09760 • Published Jun 14, 2024 • 41
BPO: Supercharging Online Preference Learning by Adhering to the Proximity of Behavior LLM

Paper • 2406.12168 • Published Jun 18, 2024 • 7
WPO: Enhancing RLHF with Weighted Preference Optimization

Paper • 2406.11827 • Published Jun 17, 2024 • 17
Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs

Paper • 2406.18629 • Published Jun 26, 2024 • 42

mDPO: Conditional Preference Optimization for Multimodal Large Language Models

Paper • 2406.11839 • Published Jun 17, 2024 • 40
Pandora: Towards General World Model with Natural Language Actions and Video States

Paper • 2406.09455 • Published Jun 12, 2024 • 16
WPO: Enhancing RLHF with Weighted Preference Optimization

Paper • 2406.11827 • Published Jun 17, 2024 • 17
In-Context Editing: Learning Knowledge from Self-Induced Distributions

Paper • 2406.11194 • Published Jun 17, 2024 • 20

WPO: Enhancing RLHF with Weighted Preference Optimization

Paper • 2406.11827 • Published Jun 17, 2024 • 17
Self-Improving Robust Preference Optimization

Paper • 2406.01660 • Published Jun 3, 2024 • 20
Bootstrapping Language Models with DPO Implicit Rewards

Paper • 2406.09760 • Published Jun 14, 2024 • 41
BPO: Supercharging Online Preference Learning by Adhering to the Proximity of Behavior LLM

Paper • 2406.12168 • Published Jun 18, 2024 • 7

Diffusion World Model

Paper • 2402.03570 • Published Feb 5, 2024 • 8
Iterative Data Smoothing: Mitigating Reward Overfitting and Overoptimization in RLHF

Paper • 2401.16335 • Published Jan 29, 2024 • 1
Towards Efficient and Exact Optimization of Language Model Alignment

Paper • 2402.00856 • Published Feb 1, 2024 • 1
ODIN: Disentangled Reward Mitigates Hacking in RLHF

Paper • 2402.07319 • Published Feb 11, 2024 • 14

Moral Foundations of Large Language Models

Paper • 2310.15337 • Published Oct 23, 2023 • 1
Specific versus General Principles for Constitutional AI

Paper • 2310.13798 • Published Oct 20, 2023 • 3
Contrastive Prefence Learning: Learning from Human Feedback without RL

Paper • 2310.13639 • Published Oct 20, 2023 • 25
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback

Paper • 2309.00267 • Published Sep 1, 2023 • 53

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