Machine Learning Street Talk (MLST)
Daniel Franzen and Jan Disselhoff, the "ARChitects" are the official winners of the ARC Prize 2024. Filmed at Tufa Labs in Zurich - they revealed how they achieved a remarkable 53.5% accuracy by creatively utilising large language models (LLMs) in new ways. Discover their innovative techniques, including depth-first search for token selection, test-time training, and a novel augmentation-based validation system. Their results were extremely surprising.
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Jan Disselhoff
https://www.linkedin.com/in/jan-disselhoff-1423a2240/
Daniel Franzen
https://github.com/da-fr
ARC Prize: http://arcprize.org/
TRANSCRIPT AND BACKGROUND READING:
https://www.dropbox.com/scl/fi/utkn2i1ma79fn6an4yvjw/ARCHitects.pdf?rlkey=67pe38mtss7oyhjk2ad0d2aza&dl=0
TOC
1. Solution Architecture and Strategy Overview
[00:00:00] 1.1 Initial Solution Overview and Model Architecture
[00:04:25] 1.2 LLM Capabilities and Dataset Approach
[00:10:51] 1.3 Test-Time Training and Data Augmentation Strategies
[00:14:08] 1.4 Sampling Methods and Search Implementation
[00:17:52] 1.5 ARC vs Language Model Context Comparison
2. LLM Search and Model Implementation
[00:21:53] 2.1 LLM-Guided Search Approaches and Solution Validation
[00:27:04] 2.2 Symmetry Augmentation and Model Architecture
[00:30:11] 2.3 Model Intelligence Characteristics and Performance
[00:37:23] 2.4 Tokenization and Numerical Processing Challenges
3. Advanced Training and Optimization
[00:45:15] 3.1 DFS Token Selection and Probability Thresholds
[00:49:41] 3.2 Model Size and Fine-tuning Performance Trade-offs
[00:53:07] 3.3 LoRA Implementation and Catastrophic Forgetting Prevention
[00:56:10] 3.4 Training Infrastructure and Optimization Experiments
[01:02:34] 3.5 Search Tree Analysis and Entropy Distribution Patterns
REFS
[00:01:05] Winning ARC 2024 solution using 12B param model, Franzen, Disselhoff, Hartmann
https://github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf
[00:03:40] Robustness of analogical reasoning in LLMs, Melanie Mitchell
https://arxiv.org/html/2411.14215
[00:07:50] Re-ARC dataset generator for ARC task variations, Michael Hodel
https://github.com/michaelhodel/re-arc
[00:15:00] Analysis of search methods in LLMs (greedy, beam, DFS), Chen et al.
https://arxiv.org/html/2408.00724v2
[00:16:55] Language model reachability space exploration, University of Toronto
https://www.youtube.com/watch?v=Bpgloy1dDn0
[00:22:30] GPT-4 guided code solutions for ARC tasks, Ryan Greenblatt
https://redwoodresearch.substack.com/p/getting-50-sota-on-arc-agi-with-gpt
[00:41:20] GPT tokenization approach for numbers, OpenAI
https://platform.openai.com/docs/guides/text-generation/tokenizer-examples
[00:46:25] DFS in AI search strategies, Russell & Norvig
https://www.amazon.com/Artificial-Intelligence-Modern-Approach-4th/dp/0134610997
[00:53:10] Paper on catastrophic forgetting in neural networks, Kirkpatrick et al.
https://www.pnas.org/doi/10.1073/pnas.1611835114
[00:54:00] LoRA for efficient fine-tuning of LLMs, Hu et al.
https://arxiv.org/abs/2106.09685
[00:57:20] NVIDIA H100 Tensor Core GPU specs, NVIDIA
https://developer.nvidia.com/blog/nvidia-hopper-architecture-in-depth/
[01:04:55] Original MCTS in computer Go, Yifan Jin
https://stanford.edu/~rezab/classes/cme323/S15/projects/montecarlo_search_tree_report.pdf