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Sakana AI Proposes DiffusionBlocks: a Block-wise Training Framework That Converts Residual Networks into Independently Trainable Denoising Modules
Researchers from Sakana AI and the University of Tokyo propose DiffusionBlocks. It trains transformer-based networks one block at a time. Training memory is reduced by…
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How to Effectively Run Many Claude Code Sessions in Parallel
coding agents sequentially and not in multiple runs in parallel, you’re losing…
Learning From Pairwise Preferences: An Introduction to the Bradley Terry Model
assumes the availability of absolute labels. For example, an instance belongs to…
They Requested It. I Built It. Nobody Ever Used It.
to us asking for a model. We built a proof of concept.…
Meet EAGLE 3.1: The Speculative Decoding Algorithm That Fixes Attention Drift in LLM Inference
Speculative decoding is a technique for speeding up large language model inference.…
MEMO: A Modular Framework for Training a Dedicated Memory Model on New Knowledge Without Modifying LLM Parameters
Large language models become static after pretraining. Their knowledge does not update…
Design a High-Precision Retrieve-and-Rerank Pipeline with ZeroEntropy Zerank-2 Reranker
print("\n" + "="*70 + "\nPART 4: NDCG@10 evaluation\n" + "="*70) eval_set =…
Stability AI Releases Stable Audio 3: A Family of Fast Latent Diffusion Models for Audio Generation and Editing
Stability AI has released open weights for Stable Audio 3 along with…
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