David Zhang
54 posts
@dzhang03
@Yale @GoogleDeepMind | prev research @david_van_dijk @calico
During its 324th graduation ceremony on Monday, Yale University awarded honorary degrees to eight individuals whose achievements in their fields have benefited the common good. This year’s honorary degree recipients are Debbie Allen, the beloved performer, producer, and director; Frances H. Arnold, a Nobel Prize-winning bioengineer and pioneer in the field of evolutionary chemistry; Ronald L. Carter, the iconic jazz musician and composer; Michael B. Curry ’78 M.Div., former presiding bishop of the Episcopal Church and longtime advocate for social justice; Henry Louis “Skip” Gates, Jr. ’73, literary scholar and award-winning filmmaker; Annette Gordon-Reed, Pulitzer Prize-winning historian and author; Fei-Fei Li, a computer scientist and pioneer in the field of artificial intelligence; and Peter B. Moore ’61, a distinguished chemist and longtime Yale professor who contributed key insights for understanding of the ribosome. The awarding of honorary degrees has been a Yale tradition since 1702. Read more about the 2025 recipients: bit.ly/3H2lhaE #Yale2025 🎓
We're missing (at least one) major paradigm for LLM learning. Not sure what to call it, possibly it has a name - system prompt learning? Pretraining is for knowledge. Finetuning (SL/RL) is for habitual behavior. Both of these involve a change in parameters but a lot of human learning feels more like a change in system prompt. You encounter a problem, figure something out, then "remember" something in fairly explicit terms for the next time. E.g. "It seems when I encounter this and that kind of a problem, I should try this and that kind of an approach/solution". It feels more like taking notes for yourself, i.e. something like the "Memory" feature but not to store per-user random facts, but general/global problem solving knowledge and strategies. LLMs are quite literally like the guy in Memento, except we haven't given them their scratchpad yet. Note that this paradigm is also significantly more powerful and data efficient because a knowledge-guided "review" stage is a significantly higher dimensional feedback channel than a reward scaler. I was prompted to jot down this shower of thoughts after reading through Claude's system prompt, which currently seems to be around 17,000 words, specifying not just basic behavior style/preferences (e.g. refuse various requests related to song lyrics) but also a large amount of general problem solving strategies, e.g.: "If Claude is asked to count words, letters, and characters, it thinks step by step before answering the person. It explicitly counts the words, letters, or characters by assigning a number to each. It only answers the person once it has performed this explicit counting step." This is to help Claude solve 'r' in strawberry etc. Imo this is not the kind of problem solving knowledge that should be baked into weights via Reinforcement Learning, or least not immediately/exclusively. And it certainly shouldn't come from human engineers writing system prompts by hand. It should come from System Prompt learning, which resembles RL in the setup, with the exception of the learning algorithm (edits vs gradient descent). A large section of the LLM system prompt could be written via system prompt learning, it would look a bit like the LLM writing a book for itself on how to solve problems. If this works it would be a new/powerful learning paradigm. With a lot of details left to figure out (how do the edits work? can/should you learn the edit system? how do you gradually move knowledge from the explicit system text to habitual weights, as humans seem to do? etc.).
