"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

Everything You Wanted to Know About LLM Post-Training, with Nathan Lambert of Allen Institute for AI

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Nov 21, 2024
Guest
Nathan Lambert
Nathan Lambert, a machine learning researcher at the Allen Institute for AI and author of the Interconnex newsletter, dives into cutting-edge post-training techniques for large language models. He discusses the Tulu project, which enhances model performance through innovative methods like supervised fine-tuning and reinforcement learning. Lambert sheds light on the significance of human feedback, the challenges of data contamination, and the collaborative nature of AI research. His insights will resonate with anyone interested in the future of AI and model optimization.
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INSIGHT

Open Source vs. Big Tech

  • OpenAI and Google's models rapidly improve, while others see incremental gains.
  • The challenge lies in understanding how open groups can effectively tackle increasing model complexity.
ADVICE

Optimizing Data Collection

  • Combine human and LLM-generated preference data.
  • Assign mechanical tasks to LLMs and more nuanced judgments to humans.
INSIGHT

Character in LLMs

  • There's ample room for post-training improvements in LLMs.
  • A key challenge is developing consistent character in models, like Claude.
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