Test-Time Adaptation: the key to reasoning with DL (Mohamed Osman)
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Mohamed Osman joins to discuss MindsAI's highest scoring entry to the ARC challenge 2024 and the paradigm of test-time fine-tuning. They explore how the team, now part of Tufa Labs in Zurich, achieved state-of-the-art results using a combination of pre-training techniques, a unique meta-learning strategy, and an ensemble voting mechanism. Mohamed emphasizes the importance of raw data input and flexibility of the network. SPONSOR MESSAGES: *** 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/ *** TRANSCRIPT + REFS: https://www.dropbox.com/scl/fi/jeavyqidsjzjgjgd7ns7h/MoFInal.pdf?rlkey=cjjmo7rgtenxrr3b46nk6yq2e&dl=0 Mohamed Osman (Tufa Labs) https://x.com/MohamedOsmanML Jack Cole (Tufa Labs) https://x.com/MindsAI_Jack How and why deep learning for ARC paper: https://github.com/MohamedOsman1998/deep-learning-for-arc/blob/main/deep_learning_for_arc.pdf TOC: 1. Abstract Reasoning Foundations [00:00:00] 1.1 Test-Time Fine-Tuning and ARC Challenge Overview [00:10:20] 1.2 Neural Networks vs Programmatic Approaches to Reasoning [00:13:23] 1.3 Code-Based Learning and Meta-Model Architecture [00:20:26] 1.4 Technical Implementation with Long T5 Model 2. ARC Solution Architectures [00:24:10] 2.1 Test-Time Tuning and Voting Methods for ARC Solutions [00:27:54] 2.2 Model Generalization and Function Generation Challenges [00:32:53] 2.3 Input Representation and VLM Limitations [00:36:21] 2.4 Architecture Innovation and Cross-Modal Integration [00:40:05] 2.5 Future of ARC Challenge and Program Synthesis Approaches 3. Advanced Systems Integration [00:43:00] 3.1 DreamCoder Evolution and LLM Integration [00:50:07] 3.2 MindsAI Team Progress and Acquisition by Tufa Labs [00:54:15] 3.3 ARC v2 Development and Performance Scaling [00:58:22] 3.4 Intelligence Benchmarks and Transformer Limitations [01:01:50] 3.5 Neural Architecture Optimization and Processing Distribution REFS: [00:01:32] Original ARC challenge paper, François Chollet https://arxiv.org/abs/1911.01547 [00:06:55] DreamCoder, Kevin Ellis et al. https://arxiv.org/abs/2006.08381 [00:12:50] Deep Learning with Python, François Chollet https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438 [00:13:35] Deep Learning with Python, François Chollet https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438 [00:13:35] Influence of pretraining data for reasoning, Laura Ruis https://arxiv.org/abs/2411.12580 [00:17:50] Latent Program Networks, Clement Bonnet https://arxiv.org/html/2411.08706v1 [00:20:50] T5, Colin Raffel et al. https://arxiv.org/abs/1910.10683 [00:30:30] Combining Induction and Transduction for Abstract Reasoning, Wen-Ding Li, Kevin Ellis et al. https://arxiv.org/abs/2411.02272 [00:34:15] Six finger problem, Chen et al. https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_SpatialVLM_Endowing_Vision-Language_Models_with_Spatial_Reasoning_Capabilities_CVPR_2024_paper.pdf [00:38:15] DeepSeek-R1-Distill-Llama, DeepSeek AI https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B [00:40:10] ARC Prize 2024 Technical Report, François Chollet et al. https://arxiv.org/html/2412.04604v2 [00:45:20] LLM-Guided Compositional Program Synthesis, Wen-Ding Li and Kevin Ellis https://arxiv.org/html/2503.15540 [00:54:25] Abstraction and Reasoning Corpus, François Chollet https://github.com/fchollet/ARC-AGI [00:57:10] O3 breakthrough on ARC-AGI, OpenAI https://arcprize.org/ [00:59:35] ConceptARC Benchmark, Arseny Moskvichev, Melanie Mitchell https://arxiv.org/abs/2305.07141 [01:02:05] Mixtape: Breaking the Softmax Bottleneck Efficiently, Yang, Zhilin and Dai, Zihang and Salakhutdinov, Ruslan and Cohen, William W. http://papers.neurips.cc/paper/9723-mixtape-breaking-the-softmax-bottleneck-efficiently.pdf
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