I think itâs still very relevant! I donât think this talkâs relevance has diminished. Itâs just important to also have that more recent information about o3 in addition to whatâs in this talk. (Thatâs why I linked the other talk at the bottom of this post.)
By the way, I think itâs just o3 and not o1 that achieves the breakthrough results on ARC-AGI-1. It looks like o1 only gets 32% on ARC-AGI-1, whereas the lower-compute version of o3 gets around 76% and the higher-compute version gets around 87%.
The lower-compute version of o3 only gets 4% on ARC-AGI-2 in partial testing (full testing has not yet been done) and the higher-compute version has not yet been tested.
Chollet speculates in this blog post about how o3 works (I donât think OpenAI has said much about this) and how that fits in to his overall thinking about LLMs and AGI:
Why does o3 score so much higher than o1? And why did o1 score so much higher than GPT-4o in the first place? I think this series of results provides invaluable data points for the ongoing pursuit of AGI.
My mental model for LLMs is that they work as a repository of vector programs. When prompted, they will fetch the program that your prompt maps to and âexecuteâ it on the input at hand. LLMs are a way to store and operationalize millions of useful mini-programs via passive exposure to human-generated content.
This âmemorize, fetch, applyâ paradigm can achieve arbitrary levels of skills at arbitrary tasks given appropriate training data, but it cannot adapt to novelty or pick up new skills on the fly (which is to say that there is no fluid intelligence at play here.) This has been exemplified by the low performance of LLMs on ARC-AGI, the only benchmark specifically designed to measure adaptability to novelty â GPT-3 scored 0, GPT-4 scored near 0, GPT-4o got to 5%. Scaling up these models to the limits of whatâs possible wasnât getting ARC-AGI numbers anywhere near what basic brute enumeration could achieve years ago (up to 50%).
To adapt to novelty, you need two things. First, you need knowledge â a set of reusable functions or programs to draw upon. LLMs have more than enough of that. Second, you need the ability to recombine these functions into a brand new program when facing a new task â a program that models the task at hand. Program synthesis. LLMs have long lacked this feature. The o series of models fixes that.
For now, we can only speculate about the exact specifics of how o3 works. But o3â˛s core mechanism appears to be natural language program search and execution within token space â at test time, the model searches over the space of possible Chains of Thought (CoTs) describing the steps required to solve the task, in a fashion perhaps not too dissimilar to AlphaZero-style Monte-Carlo tree search. In the case of o3, the search is presumably guided by some kind of evaluator model. To note, Demis Hassabis hinted back in a June 2023 interview that DeepMind had been researching this very idea â this line of work has been a long time coming.
So while single-generation LLMs struggle with novelty, o3 overcomes this by generating and executing its own programs, where the program itself (the CoT) becomes the artifact of knowledge recombination. Although this is not the only viable approach to test-time knowledge recombination (you could also do test-time training, or search in latent space), it represents the current state-of-the-art as per these new ARC-AGI numbers.
Effectively, o3 represents a form of deep learning-guided program search. The model does test-time search over a space of âprogramsâ (in this case, natural language programs â the space of CoTs that describe the steps to solve the task at hand), guided by a deep learning prior (the base LLM). The reason why solving a single ARC-AGI task can end up taking up tens of millions of tokens and cost thousands of dollars is because this search process has to explore an enormous number of paths through program space â including backtracking.
There are however two significant differences between whatâs happening here and what I meant when I previously described âdeep learning-guided program searchâ as the best path to get to AGI. Crucially, the programs generated by o3 are natural language instructions (to be âexecutedâ by a LLM) rather than executable symbolic programs. This means two things. First, that they cannot make contact with reality via execution and direct evaluation on the task â instead, they must be evaluated for fitness via another model, and the evaluation, lacking such grounding, might go wrong when operating out of distribution. Second, the system cannot autonomously acquire the ability to generate and evaluate these programs (the way a system like AlphaZero can learn to play a board game on its own.) Instead, it is reliant on expert-labeled, human-generated CoT data.
Itâs not yet clear what the exact limitations of the new system are and how far it might scale. Weâll need further testing to find out. Regardless, the current performance represents a remarkable achievement, and a clear confirmation that intuition-guided test-time search over program space is a powerful paradigm to build AI systems that can adapt to arbitrary tasks.
I think itâs still very relevant! I donât think this talkâs relevance has diminished. Itâs just important to also have that more recent information about o3 in addition to whatâs in this talk. (Thatâs why I linked the other talk at the bottom of this post.)
By the way, I think itâs just o3 and not o1 that achieves the breakthrough results on ARC-AGI-1. It looks like o1 only gets 32% on ARC-AGI-1, whereas the lower-compute version of o3 gets around 76% and the higher-compute version gets around 87%.
The lower-compute version of o3 only gets 4% on ARC-AGI-2 in partial testing (full testing has not yet been done) and the higher-compute version has not yet been tested.
Chollet speculates in this blog post about how o3 works (I donât think OpenAI has said much about this) and how that fits in to his overall thinking about LLMs and AGI: