LLMs Don't Need More Parameters. They Need Loops. - YouTube
This NeuroDump video challenges the conventional wisdom of scaling LLMs. It explores a novel looped architecture, analyzing its training and inference processes. Discover how this approach might offer a new scaling dimension, potentially improving performance without massive parameter increases. Follow along using the transcript. This NeuroDump video challenges the conventional wisdom of scaling LLMs. It explores a novel looped architecture, analyzing its training and inference processes. Discover how this approach might offer a new scaling dimension, potentially improving performance without massive parameter increases. Follow along using the transcript.
A deep dive into how looped language models change the scaling game. Our paper "Scaling Latent Reasoning via Looped Language Models" goes into a lot more depth and can be found on arXiv: https://arxiv.org/abs/2510.25741 Ouro Homepage: https://ouro-llm.github.io/ Video developed by Jason Eshraghian and Rui-Jie Zhu. --- Papers cited in the video: Scaling Laws for Neural Language Models: https://arxiv.org/abs/2001.08361 Will we run out of data? Limits of LLM scaling based on human-generated data: https://arxiv.org/abs/2211.04325 Does Reinforcement Learning Really Incentivize Reasoning…
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