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Collections including paper arxiv:2404.03715

Reinforcement Learning (RL / RLHF)

RLHF Workflow: From Reward Modeling to Online RLHF

Paper • 2405.07863 • Published May 13 • 67
Understanding and Diagnosing Deep Reinforcement Learning

Paper • 2406.16979 • Published Jun 23 • 9
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 60
Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning

Paper • 2407.00617 • Published Jun 30 • 7

Best Practices and Lessons Learned on Synthetic Data for Language Models

Paper • 2404.07503 • Published Apr 11 • 29
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 60
Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing

Paper • 2406.08464 • Published Jun 12 • 65
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

Paper • 2402.13064 • Published Feb 20 • 46

mDPO: Conditional Preference Optimization for Multimodal Large Language Models

Paper • 2406.11839 • Published Jun 17 • 37
Pandora: Towards General World Model with Natural Language Actions and Video States

Paper • 2406.09455 • Published Jun 12 • 14
WPO: Enhancing RLHF with Weighted Preference Optimization

Paper • 2406.11827 • Published Jun 17 • 14
In-Context Editing: Learning Knowledge from Self-Induced Distributions

Paper • 2406.11194 • Published Jun 17 • 15

Rho-1: Not All Tokens Are What You Need

Paper • 2404.07965 • Published Apr 11 • 84
VASA-1: Lifelike Audio-Driven Talking Faces Generated in Real Time

Paper • 2404.10667 • Published Apr 16 • 17
Instruction-tuned Language Models are Better Knowledge Learners

Paper • 2402.12847 • Published Feb 20 • 24
DoRA: Weight-Decomposed Low-Rank Adaptation

Paper • 2402.09353 • Published Feb 14 • 26

Synthetic Data and Self-Improvement

Self-Rewarding Language Models

Paper • 2401.10020 • Published Jan 18 • 143
Self-Discover: Large Language Models Self-Compose Reasoning Structures

Paper • 2402.03620 • Published Feb 6 • 109
OS-Copilot: Towards Generalist Computer Agents with Self-Improvement

Paper • 2402.07456 • Published Feb 12 • 41
Learning From Mistakes Makes LLM Better Reasoner

Paper • 2310.20689 • Published Oct 31, 2023 • 28

Papers-Fundamentals

RoFormer: Enhanced Transformer with Rotary Position Embedding

Paper • 2104.09864 • Published Apr 20, 2021 • 10
Attention Is All You Need

Paper • 1706.03762 • Published Jun 12, 2017 • 44
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 60
Zero-Shot Tokenizer Transfer

Paper • 2405.07883 • Published May 13 • 4

Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 60

Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 60

RecurrentGemma: Moving Past Transformers for Efficient Open Language Models

Paper • 2404.07839 • Published Apr 11 • 41
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 60
MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation

Paper • 2404.05674 • Published Apr 8 • 13
Agentless: Demystifying LLM-based Software Engineering Agents

Paper • 2407.01489 • Published Jul 1 • 42

Papers - RLHF - Iterative Contrastive Self-Improvement

A batched on-policy algorithm that conducts self-improvement iteratively via contrastive learning

Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 60

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