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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 26 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 13 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 43 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 22
Collections
Discover the best community collections!
Collections including paper arxiv:2406.20095
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RLHF Workflow: From Reward Modeling to Online RLHF
Paper • 2405.07863 • Published • 68 -
Understanding and Diagnosing Deep Reinforcement Learning
Paper • 2406.16979 • Published • 9 -
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
Paper • 2404.03715 • Published • 61 -
Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning
Paper • 2407.00617 • Published • 7
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LLaRA: Supercharging Robot Learning Data for Vision-Language Policy
Paper • 2406.20095 • Published • 18 -
variante/llava-1.5-7b-llara-D-inBC-Aux-B-VIMA-80k
Image-Text-to-Text • Updated • 14 • 2 -
variante/llava-1.5-7b-llara-D-inBC-Aux-D-VIMA-80k
Image-Text-to-Text • Updated • 12 • 1 -
variante/llava-1.5-7b-llara-D-inBC-VIMA-80k
Image-Text-to-Text • Updated • 13 • 1
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World Model on Million-Length Video And Language With RingAttention
Paper • 2402.08268 • Published • 38 -
Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 80 -
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 105 -
FiT: Flexible Vision Transformer for Diffusion Model
Paper • 2402.12376 • Published • 48
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PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models
Paper • 2402.08714 • Published • 14 -
Data Engineering for Scaling Language Models to 128K Context
Paper • 2402.10171 • Published • 25 -
RLVF: Learning from Verbal Feedback without Overgeneralization
Paper • 2402.10893 • Published • 12 -
Coercing LLMs to do and reveal (almost) anything
Paper • 2402.14020 • Published • 13