AI Summer Nathan Lambert on the rise of "thinking" language models
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Jan 14, 2025 
Nathan Lambert, a research scientist and author of the AI newsletter Interconnects, dives into the fascinating world of language model evolution. He breaks down the shift from pre-training to innovative post-training techniques, emphasizing the complexities of instruction tuning and diverse data usage. Lambert discusses the advancements in reinforcement learning that enhance reasoning capabilities and the balance between scaling models and innovative techniques. He also touches on ethical considerations and the quest for artificial general intelligence amidst the growing field of AI.
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Post-Training's Importance
- Post-training is crucial for refining large language models (LLMs).
- Nathan Lambert, leading post-training at AI2, offers valuable insights.
Evolving Training Methods
- Pre-training will become more specialized as models evolve.
- The line between pre- and post-training is blurring, with techniques like large-scale post-training emerging.
Raw Model Behavior
- Raw, pre-trained language models function like autocomplete, predicting next words.
- They lack the predictable behavior of refined chatbots.