Daniel Franzen & Jan Disselhoff - ARC Prize 2024 winners
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. SPONSOR MESSAGES: *** CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Check out their super fast DeepSeek R1 hosting! https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** 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