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Collections including paper arxiv:2405.08448

EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters

Paper • 2402.04252 • Published Feb 6 • 25
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models

Paper • 2402.03749 • Published Feb 6 • 12
ScreenAI: A Vision-Language Model for UI and Infographics Understanding

Paper • 2402.04615 • Published Feb 7 • 36
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

Paper • 2402.05008 • Published Feb 7 • 19

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

reinforcement learning

Understanding the performance gap between online and offline alignment algorithms

Paper • 2405.08448 • Published May 14 • 14
OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework

Paper • 2405.11143 • Published May 20 • 33

Understanding the performance gap between online and offline alignment algorithms

Paper • 2405.08448 • Published May 14 • 14
Self-Exploring Language Models: Active Preference Elicitation for Online Alignment

Paper • 2405.19332 • Published May 29 • 15
Offline Regularised Reinforcement Learning for Large Language Models Alignment

Paper • 2405.19107 • Published May 29 • 13
Show, Don't Tell: Aligning Language Models with Demonstrated Feedback

Paper • 2406.00888 • Published Jun 2 • 30

Paper Reading List

xLSTM: Extended Long Short-Term Memory

Paper • 2405.04517 • Published May 7 • 10
You Only Cache Once: Decoder-Decoder Architectures for Language Models

Paper • 2405.05254 • Published May 8 • 9
Understanding the performance gap between online and offline alignment algorithms

Paper • 2405.08448 • Published May 14 • 14
Chameleon: Mixed-Modal Early-Fusion Foundation Models

Paper • 2405.09818 • Published May 16 • 125

Foundation AI Papers (II)

Iterative Reasoning Preference Optimization

Paper • 2404.19733 • Published Apr 30 • 46
Better & Faster Large Language Models via Multi-token Prediction

Paper • 2404.19737 • Published Apr 30 • 73
ORPO: Monolithic Preference Optimization without Reference Model

Paper • 2403.07691 • Published Mar 12 • 61
KAN: Kolmogorov-Arnold Networks

Paper • 2404.19756 • Published Apr 30 • 108

OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement

Paper • 2402.14658 • Published Feb 22 • 82
KAN: Kolmogorov-Arnold Networks

Paper • 2404.19756 • Published Apr 30 • 108
Understanding the performance gap between online and offline alignment algorithms

Paper • 2405.08448 • Published May 14 • 14
NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

Paper • 2405.17428 • Published May 27 • 16

ORPO: Monolithic Preference Optimization without Reference Model

Paper • 2403.07691 • Published Mar 12 • 61
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28 • 38
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 • 29

A Critical Evaluation of AI Feedback for Aligning Large Language Models

Paper • 2402.12366 • Published Feb 19 • 3
PERL: Parameter Efficient Reinforcement Learning from Human Feedback

Paper • 2403.10704 • Published Mar 15 • 56
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Paper • 2403.03507 • Published Mar 6 • 182
Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation

Paper • 2401.08417 • Published Jan 16 • 31

Interesting things.

AtP*: An efficient and scalable method for localizing LLM behaviour to components

Paper • 2403.00745 • Published Mar 1 • 11
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Paper • 2402.17764 • Published Feb 27 • 596
MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT

Paper • 2402.16840 • Published Feb 26 • 23
LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens

Paper • 2402.13753 • Published Feb 21 • 111

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