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deter3When critics argue that Large Language Models (LLMs) cannot truly reason or plan, they may be setting an unrealistic standard. Here's why: Most human work relies on pattern recognition and applying learned solutions to familiar problems. Only a small percentage of tasks require…
Chenxin An🚀 Unlocking Longer Effective Contexts in LLMs! Ever wondered why your LLM’s effective context length doesn’t match its training length? 🤔 Our latest research uncovers that these infrequent positions greatly limit the model's ability to use distant information. ✨ Introducing… https://t.co/2alKa4rexQ
Sean HendryxReasoning at length will be a key part of LLMs solving more challenging problems, but how can we make sure that their chain of thought stays on track? At @scale_AI, we’ve developed a method to learn token-wise expected rewards from pairwise preference labels 🧵


