LLMs Don't Need More Parameters. They Need Loops. скачать в хорошем качестве

LLMs Don't Need More Parameters. They Need Loops.

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 Capacity in LLMs Beyond the Base Model?: https://arxiv.org/abs/2504.13837 Universal Transformers: https://arxiv.org/abs/1807.03819 PonderNet: Learning to Ponder: https://arxiv.org/abs/2107.05407 Physics of Language Models: https://physics.allen-zhu.com/ --- Timestamps 0:00 - Scaling Laws 1:42 - The Data Wall 2:40 - Reasoning and its Problems 6:50 - Looped LLMs 9:30 - Dynamic Termination 13:13 - Reward Hacking 15:09 - Entropy Regularization 16:42 - Looped KV Caching 19:41 - Training Pipeline 20:44 - Results 22:43 - Physics of LLMs