Large language models (or LLMs) have become a topic of daily conversations. Their quick adoption is evident by the amount …
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Frugality meets Accuracy: Cost-efficient training of GPT NeoX and Pythia models with AWS Trainium
Koala: A Dialogue Model for Academic Research
In this post, we introduce Koala, a chatbot trained by fine-tuning Meta’s LLaMA on dialogue data gathered from the web. …
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Interactive Fleet Learning
Figure 1: “Interactive Fleet Learning” (IFL) refers to robot fleets in industry and academia that fall back on human teleoperators …
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GPT-4 + Stable-Diffusion = ?: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models
TL;DR: Text Prompt -> LLM -> Intermediate Representation (such as an image layout) -> Stable Diffusion -> Image. Recent advancements …
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Generating 3D Molecular Conformers via Equivariant Coarse-Graining and Aggregated Attention
<!– –> Figure 1: CoarsenConf architecture. <!– (I) The encoder $q_phi(z| X, mathcal{R})$ takes the fine-grained (FG) ground truth conformer …
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On the Stepwise Nature of <br> Self-Supervised Learning
Figure 1: stepwise behavior in self-supervised learning. When training common SSL algorithms, we find that the loss descends in a …
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Training Diffusion Models with <br> Reinforcement Learning
Training Diffusion Models with Reinforcement Learning replay Diffusion models have recently emerged as the de facto standard for generating complex, …
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Rethinking the Role of PPO in RLHF
Rethinking the Role of PPO in RLHF TL;DR: In RLHF, there’s tension between the reward learning phase, which uses human …
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Goal Representations for Instruction Following
Goal Representations for Instruction Following <!– Figure title. Figure caption. This image is centered and set to 50% page width. …
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Asymmetric Certified Robustness via Feature-Convex Neural Networks
Asymmetric Certified Robustness via Feature-Convex Neural Networks TLDR: We propose the asymmetric certified robustness problem, which requires certified robustness for …
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