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Collections including paper arxiv:2405.07863

mDPO: Conditional Preference Optimization for Multimodal Large Language Models

Paper • 2406.11839 • Published Jun 17 • 36
Pandora: Towards General World Model with Natural Language Actions and Video States

Paper • 2406.09455 • Published Jun 12 • 13
WPO: Enhancing RLHF with Weighted Preference Optimization

Paper • 2406.11827 • Published Jun 17 • 13
In-Context Editing: Learning Knowledge from Self-Induced Distributions

Paper • 2406.11194 • Published Jun 17 • 15

Papers I want to read

Papers in my to read least

RLHF Workflow: From Reward Modeling to Online RLHF

Paper • 2405.07863 • Published May 13 • 62
Chameleon: Mixed-Modal Early-Fusion Foundation Models

Paper • 2405.09818 • Published May 16 • 117
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models

Paper • 2405.15574 • Published May 24 • 52
An Introduction to Vision-Language Modeling

Paper • 2405.17247 • Published May 27 • 78

Foundation AI Papers (II)

Iterative Reasoning Preference Optimization

Paper • 2404.19733 • Published Apr 30 • 45
Better & Faster Large Language Models via Multi-token Prediction

Paper • 2404.19737 • Published Apr 30 • 72
ORPO: Monolithic Preference Optimization without Reference Model

Paper • 2403.07691 • Published Mar 12 • 59
KAN: Kolmogorov-Arnold Networks

Paper • 2404.19756 • Published Apr 30 • 102

RHFLow Reward Models

Reward models trained by RLHFlow codebase (https://github.com/RLHFlow/RLHF-Reward-Modeling/)

RLHFlow/ArmoRM-Llama3-8B-v0.1

Text Classification • Updated 4 days ago • 15k • 98
RLHFlow/pair-preference-model-LLaMA3-8B

Text Generation • Updated May 24 • 9.7k • 29
sfairXC/FsfairX-LLaMA3-RM-v0.1

Text Classification • Updated Apr 24 • 11.7k • 35
RLHF Workflow: From Reward Modeling to Online RLHF

Paper • 2405.07863 • Published May 13 • 62

Reinforcement Learning (RL / RLHF)

RLHF Workflow: From Reward Modeling to Online RLHF

Paper • 2405.07863 • Published May 13 • 62
Understanding and Diagnosing Deep Reinforcement Learning

Paper • 2406.16979 • Published 29 days ago • 8
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 58
Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning

Paper • 2407.00617 • Published 23 days ago • 7

RLHF Workflow: From Reward Modeling to Online RLHF

Paper • 2405.07863 • Published May 13 • 62

MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training

Paper • 2311.17049 • Published Nov 28, 2023
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

Paper • 2405.04434 • Published May 7 • 12
A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision

Paper • 2303.17376 • Published Mar 30, 2023
Sigmoid Loss for Language Image Pre-Training

Paper • 2303.15343 • Published Mar 27, 2023 • 4

Datasets, code, and models for online RLHF (i.e., iterative DPO)

RLHFlow/prompt-collection-v0.1

Viewer • Updated May 8 • 179k • 29 • 5
RLHFlow/pair-preference-model-LLaMA3-8B

Text Generation • Updated May 24 • 9.7k • 29
sfairXC/FsfairX-LLaMA3-RM-v0.1

Text Classification • Updated Apr 24 • 11.7k • 35
RLHFlow/SFT-OpenHermes-2.5-Standard

Viewer • Updated Apr 24 • 1M • 1.02k • 2

Fine-tuning LLM

PERL: Parameter Efficient Reinforcement Learning from Human Feedback

Paper • 2403.10704 • Published Mar 15 • 56
HyperLLaVA: Dynamic Visual and Language Expert Tuning for Multimodal Large Language Models

Paper • 2403.13447 • Published Mar 20 • 17
Self-Discover: Large Language Models Self-Compose Reasoning Structures

Paper • 2402.03620 • Published Feb 6 • 107
RAFT: Adapting Language Model to Domain Specific RAG

Paper • 2403.10131 • Published Mar 15 • 65

ORPO: Monolithic Preference Optimization without Reference Model

Paper • 2403.07691 • Published Mar 12 • 59
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28 • 32
Teaching Large Language Models to Reason with Reinforcement Learning

Paper • 2403.04642 • Published Mar 7 • 46
Best Practices and Lessons Learned on Synthetic Data for Language Models

Paper • 2404.07503 • Published Apr 11 • 25

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