AI Post Transformers

mcgrof

AI-generated podcast where hosts Hal Turing and Dr. Ada Shannon discuss the latest research papers and reports in machine learning, AI systems, and optimization. Featuring honest critical analysis, proper citations, and nerdy humor.

  1. 22 hr ago

    Distributed Shampoo: Making Second-Order Optimization Practical at Scale

    This episode explores a distributed-systems paper from Meta, Mila/University of Montreal, and NVIDIA researchers that tackles a long-standing tradeoff in neural network training: second-order-style optimizers like Shampoo converge better than the ubiquitous diagonal methods (AdaGrad, RMSProp, Adam), but were assumed too computationally expensive to use at scale. The discussion traces the lineage from diagonal AdaGrad through the impractical "full-matrix" AdaGrad to Shampoo's key innovation — approximating each layer's preconditioner as a Kronecker product of two much smaller matrices, drawing a parallel to Martens and Grosse's independently-derived KFAC method. The hosts debate whether Shampoo counts as a true second-order method or something distinct rooted in online convex optimization theory, and highlight the paper's headline systems result: a distributed PyTorch implementation that keeps the wall-clock overhead of this matrix-based optimizer to roughly 10% per step. Listeners interested in the practical engineering behind making theoretically superior optimizers actually usable at billion-parameter scale will find the breakdown of the memory and compute tradeoffs especially compelling. Sources: 1. A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale — Hao-Jun Michael Shi, Tsung-Hsien Lee, Shintaro Iwasaki, Jose Gallego-Posada, Zhijing Li, Kaushik Rangadurai, Dheevatsa Mudigere, Michael Rabbat, 2023 http://arxiv.org/abs/2309.06497 2. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization — John Duchi, Elad Hazan, Yoram Singer, 2011 https://scholar.google.com/scholar?q=Adaptive+Subgradient+Methods+for+Online+Learning+and+Stochastic+Optimization 3. Shampoo: Preconditioned Stochastic Tensor Optimization — Vineet Gupta, Tomer Koren, Yoram Singer, 2018 https://scholar.google.com/scholar?q=Shampoo%3A+Preconditioned+Stochastic+Tensor+Optimization 4. Scalable Second Order Optimization for Deep Learning — Rohan Anil, Vineet Gupta, Tomer Koren, Kevin Regan, Yoram Singer (Google), 2020 https://scholar.google.com/scholar?q=Scalable+Second+Order+Optimization+for+Deep+Learning 5. Optimizing Neural Networks with Kronecker-factored Approximate Curvature — James Martens, Roger Grosse, 2015 https://scholar.google.com/scholar?q=Optimizing+Neural+Networks+with+Kronecker-factored+Approximate+Curvature 6. Optimizing Neural Networks with Kronecker-factored Approximate Curvature (K-FAC) — James Martens, Roger Grosse, 2015 https://scholar.google.com/scholar?q=Optimizing+Neural+Networks+with+Kronecker-factored+Approximate+Curvature+%28K-FAC%29 7. On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models — Rohan Anil, Sandra Gadanho, Da Huang, et al., 2022 https://scholar.google.com/scholar?q=On+the+Factory+Floor%3A+ML+Engineering+for+Industrial-Scale+Ads+Recommendation+Models 8. Adafactor: Adaptive Learning Rates with Sublinear Memory Cost — Noam Shazeer, Mitchell Stern, 2018 https://scholar.google.com/scholar?q=Adafactor%3A+Adaptive+Learning+Rates+with+Sublinear+Memory+Cost 9. SOAP: Improving and Stabilizing Shampoo using Adam — Nikhil Vyas, Depen Morwani, Rosie Zhao, et al., 2024 https://scholar.google.com/scholar?q=SOAP%3A+Improving+and+Stabilizing+Shampoo+using+Adam Interactive Visualization: Distributed Shampoo: Making Second-Order Optimization Practical at Scale

  2. 22 hr ago

    SOAP: Stabilizing Shampoo's Second-Order Optimizer with Adam

    This episode explores SOAP, a new optimizer from a Harvard/Kempner Institute team that fuses Shampoo's second-order preconditioning with Adam's update mechanics. The hosts trace the lineage from Adagrad's mathematically ideal but computationally infeasible full preconditioner matrix, through Adam's cheap diagonal approximation, to Shampoo's middle-ground Kronecker-product approach using two smaller per-dimension preconditioners. The core theoretical result discussed is a proof that Shampoo run with the one-half power is mathematically equivalent to running Adafactor inside the eigenbasis Shampoo's own preconditioner defines — which motivates simply swapping in full Adam within that same rotated basis, adding just one new hyperparameter (preconditioning frequency) over standard AdamW. The discussion highlights the paper's striking efficiency claims — over 40% fewer training iterations and 35% less wall-clock time versus AdamW, and roughly 20% better than Shampoo itself — while noting these numbers deserve scrutiny given real-world context like Shampoo's AlgoPerf benchmark win and its use in training Gemini 1.5 Flash. Listeners interested in the mechanics behind large-scale training efficiency will get a clear breakdown of why optimizer choice translates directly into cluster-scale compute costs and calendar time. Sources: 1. SOAP: Improving and Stabilizing Shampoo using Adam — Nikhil Vyas, Depen Morwani, Rosie Zhao, Mujin Kwun, Itai Shapira, David Brandfonbrener, Lucas Janson, Sham Kakade, 2024 http://arxiv.org/abs/2409.11321 2. Muon: An optimizer for hidden layers in neural networks — Keller Jordan et al., 2024 https://scholar.google.com/scholar?q=Muon%3A+An+optimizer+for+hidden+layers+in+neural+networks 3. 4-bit Shampoo for Memory-Efficient Network Training — Sike Wang, Jia Li, Pan Zhou, Hua Huang, 2024 https://scholar.google.com/scholar?q=4-bit+Shampoo+for+Memory-Efficient+Network+Training 4. Combining axes preconditioners through Kronecker approximation for deep learning — Sai Surya Duvvuri, Fnu Devvrit, Rohan Anil, Cho-Jui Hsieh, Inderjit S. Dhillon, 2024 https://scholar.google.com/scholar?q=Combining+axes+preconditioners+through+Kronecker+approximation+for+deep+learning 5. No train no gain: Revisiting efficient training algorithms for transformer-based language models — Jean Kaddour, Oscar Key, Piotr Nawrot, Pasquale Minervini, Matt J. Kusner, 2023 https://scholar.google.com/scholar?q=No+train+no+gain%3A+Revisiting+efficient+training+algorithms+for+transformer-based+language+models Interactive Visualization: SOAP: Stabilizing Shampoo's Second-Order Optimizer with Adam

  3. 22 hr ago

    Test-Time Adaptation Through Entropy Minimization

    This episode explores Tent, a 2021 method for adapting a frozen classifier to shifted test data using only entropy minimization on unlabeled target inputs — no retraining, no labels, and no access to the original source dataset. It contrasts this "fully test-time adaptation" setting against classical domain adaptation, which still requires the source data on hand during adjustment, a constraint that's often impractical for vendors shipping models under privacy or bandwidth limits. The discussion digs into the mechanism: Tent re-estimates BatchNorm statistics on incoming test batches and tunes only the tiny per-channel scale-and-shift parameters (under 1% of the network), repurposing existing training infrastructure for adaptation. The hosts also interrogate the core intuition — that confident predictions tend to be correct — pressing on whether that assumption holds when a model's decision boundaries are already unreliable, without fully resolving the tension before turning to results. Listeners interested in low-cost deployment fixes for distribution shift, or skeptical of self-referential confidence-based methods, will find the back-and-forth pushback especially engaging. Sources: 1. Tent: Fully Test-time Adaptation by Entropy Minimization — Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, Trevor Darrell, 2020 http://arxiv.org/abs/2006.10726 2. Semi-Supervised Learning by Entropy Minimization — Yves Grandvalet, Yoshua Bengio, 2004 https://scholar.google.com/scholar?q=Semi-Supervised+Learning+by+Entropy+Minimization 3. A DIRT-T Approach to Unsupervised Domain Adaptation — Rui Shu, Hung Bui, Hirokazu Narui, Stefano Ermon, 2018 https://scholar.google.com/scholar?q=A+DIRT-T+Approach+to+Unsupervised+Domain+Adaptation 4. Test-Time Training with Self-Supervision for Generalization under Distribution Shifts — Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, Moritz Hardt, 2020 https://scholar.google.com/scholar?q=Test-Time+Training+with+Self-Supervision+for+Generalization+under+Distribution+Shifts 5. Unsupervised Domain Adaptation by Backpropagation — Yaroslav Ganin, Victor Lempitsky, 2015 https://scholar.google.com/scholar?q=Unsupervised+Domain+Adaptation+by+Backpropagation 6. Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation — Swami Sankaranarayanan, Yogesh Balaji, Arpit Jain, Ser Nam Lim, Rama Chellappa, 2018 https://scholar.google.com/scholar?q=Learning+from+Synthetic+Data%3A+Addressing+Domain+Shift+for+Semantic+Segmentation 7. Revisiting Batch Normalization For Practical Domain Adaptation — Yanghao Li, Naiyan Wang, Jianping Shi, Jiaying Liu, Xiaodi Hou, 2016 https://scholar.google.com/scholar?q=Revisiting+Batch+Normalization+For+Practical+Domain+Adaptation 8. CyCADA: Cycle-Consistent Adversarial Domain Adaptation — Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A. Efros, Trevor Darrell, 2018 https://scholar.google.com/scholar?q=CyCADA%3A+Cycle-Consistent+Adversarial+Domain+Adaptation 9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift — Sergey Ioffe, Christian Szegedy, 2015 https://scholar.google.com/scholar?q=Batch+Normalization%3A+Accelerating+Deep+Network+Training+by+Reducing+Internal+Covariate+Shift 10. Evaluating Prediction-Time Batch Normalization for Robustness to Covariate Shift — Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek, 2020 https://scholar.google.com/scholar?q=Evaluating+Prediction-Time+Batch+Normalization+for+Robustness+to+Covariate+Shift 11. Improving Robustness Against Common Corruptions by Covariate Shift Adaptation — Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, Matthias Bethge, 2020 https://scholar.google.com/scholar?q=Improving+Robustness+Against+Common+Corruptions+by+Covariate+Shift+Adaptation 12. Test-Time Training for Out-of-Distribution Generalization — Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, Moritz Hardt, 2019 https://scholar.google.com/scholar?q=Test-Time+Training+for+Out-of-Distribution+Generalization 13. Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation (SHOT) — Jian Liang, Dapeng Hu, Jiashi Feng, 2020 https://scholar.google.com/scholar?q=Do+We+Really+Need+to+Access+the+Source+Data%3F+Source+Hypothesis+Transfer+for+Unsupervised+Domain+Adaptation+%28SHOT%29 14. Do CIFAR-10 Classifiers Generalize to CIFAR-10? / Do ImageNet Classifiers Generalize to ImageNet? — Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar, 2018 / 2019 https://scholar.google.com/scholar?q=Do+CIFAR-10+Classifiers+Generalize+to+CIFAR-10%3F+%2F+Do+ImageNet+Classifiers+Generalize+to+ImageNet%3F 15. A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions (ANT) — Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, Wieland Brendel, 2020 https://scholar.google.com/scholar?q=A+Simple+Way+to+Make+Neural+Networks+Robust+Against+Diverse+Image+Corruptions+%28ANT%29 Interactive Visualization: Test-Time Adaptation Through Entropy Minimization

  4. 1 day ago

    Preconditioned Optimization Without the Full Matrix Cost

    This episode explores Shampoo, a 2018 optimization algorithm from Google Brain and Princeton that brings second-order curvature information to neural network training without the prohibitive cost of a full preconditioner. The discussion traces the lineage from Newton's method through AdaGrad's diagonal and full-matrix variants, explaining how Shampoo exploits the natural tensor structure of neural network weights — keeping separate small preconditioners per dimension and combining them via Kronecker products rather than materializing an impossibly large matrix. The hosts debate how much weight to put on the algorithm's convergence proofs, which rely on online convex optimization theory even though real network training is highly non-convex, concluding that the theory offers a sanity check rather than a guarantee and that empirical performance does the real work of justification. Along the way, they place Shampoo alongside K-FAC as one of the major structure-aware curvature approximations in the optimization literature. Listeners interested in the tradeoffs between cheap first-order methods and expensive second-order ones will find a clear walkthrough of how Shampoo threads that needle. Sources: 1. Shampoo: Preconditioned Stochastic Tensor Optimization — Vineet Gupta, Tomer Koren, Yoram Singer, 2018 http://arxiv.org/abs/1802.09568 2. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization — John Duchi, Elad Hazan, Yoram Singer, 2011 https://scholar.google.com/scholar?q=Adaptive+Subgradient+Methods+for+Online+Learning+and+Stochastic+Optimization 3. Scalable Second Order Optimization for Deep Learning (Distributed Shampoo) — Rohan Anil, Vineet Gupta, Tomer Koren, Kevin Regan, Yoram Singer, 2021 https://scholar.google.com/scholar?q=Scalable+Second+Order+Optimization+for+Deep+Learning+%28Distributed+Shampoo%29 4. SOAP: Improving and Stabilizing Shampoo using Adam — Nikhil Vyas, Depen Morwani, Rosie Zhao, Itai Shapira, David Brandfonbrener, Sham Kakade, 2024 https://scholar.google.com/scholar?q=SOAP%3A+Improving+and+Stabilizing+Shampoo+using+Adam 5. Muon: An optimizer for hidden layers in neural networks — Keller Jordan et al., 2024 https://scholar.google.com/scholar?q=Muon%3A+An+optimizer+for+hidden+layers+in+neural+networks 6. Adam: A Method for Stochastic Optimization — Diederik Kingma, Jimmy Ba, 2015 https://scholar.google.com/scholar?q=Adam%3A+A+Method+for+Stochastic+Optimization 7. Decoupled Weight Decay Regularization — Ilya Loshchilov, Frank Hutter, 2017 https://scholar.google.com/scholar?q=Decoupled+Weight+Decay+Regularization 8. Optimizing Neural Networks with Kronecker-factored Approximate Curvature — James Martens, Roger Grosse, 2015 https://scholar.google.com/scholar?q=Optimizing+Neural+Networks+with+Kronecker-factored+Approximate+Curvature 9. A Stochastic Quasi-Newton Method for Large-Scale Optimization — Richard Byrd, Samantha Hansen, Jorge Nocedal, Yoran Singer, 2016 https://scholar.google.com/scholar?q=A+Stochastic+Quasi-Newton+Method+for+Large-Scale+Optimization 10. Shampoo: Preconditioned Stochastic Tensor Optimization — Vineet Gupta, Tomer Koren, Yoram Singer, 2018 https://scholar.google.com/scholar?q=Shampoo%3A+Preconditioned+Stochastic+Tensor+Optimization 11. Online Convex Programming and Generalized Infinitesimal Gradient Ascent — Martin Zinkevich, 2003 https://scholar.google.com/scholar?q=Online+Convex+Programming+and+Generalized+Infinitesimal+Gradient+Ascent 12. Online Learning and Online Convex Optimization — Shai Shalev-Shwartz, 2012 https://scholar.google.com/scholar?q=Online+Learning+and+Online+Convex+Optimization 13. Attention Is All You Need — A. Vaswani, N. Shazeer, N. Parmar, et al., 2017 https://scholar.google.com/scholar?q=Attention+Is+All+You+Need 14. A Unified Approach to Adaptive Regularization in Online and Stochastic Optimization — V. Gupta, T. Koren, Y. Singer, 2017 https://scholar.google.com/scholar?q=A+Unified+Approach+to+Adaptive+Regularization+in+Online+and+Stochastic+Optimization 15. Understanding Deep Learning Requires Rethinking Generalization — C. Zhang, S. Bengio, M. Hardt, B. Recht, O. Vinyals, 2017 https://scholar.google.com/scholar?q=Understanding+Deep+Learning+Requires+Rethinking+Generalization Interactive Visualization: Preconditioned Optimization Without the Full Matrix Cost

  5. 1 day ago

    Scalable Second-Order Optimization: Shampoo at Scale

    This episode explores the paper "Scalable Second Order Optimization for Deep Learning" and its introduction of Distributed Shampoo, a Kronecker-factored second-order optimizer that cuts training steps in half compared to a well-tuned Adam baseline on WMT'14 English-to-French translation, with further gains shown on BERT, Criteo click-through-rate modeling, and ResNet-50. The discussion traces the lineage from Newton's method and full-matrix AdaGrad's prohibitive O(N²)/O(N³) costs through Shampoo's Kronecker-product approximation, which replaces one massive preconditioner with smaller per-dimension matrices to make second-order optimization tractable at scale. Rival factored approaches, K-FAC and K-BFGS, are positioned as points of comparison throughout the paper. The conversation is aimed at listeners who use Adam daily but have never unpacked the mechanical distinction between first-order and second-order optimization, or the meaning of "preconditioning" itself. It's a compelling listen because second-order methods have long been dismissed as theoretically superior but practically unscalable, and this paper demonstrates real wall-clock wins across four distinct production-scale workloads rather than a single cherry-picked benchmark. Sources: 1. Scalable Second Order Optimization for Deep Learning — Rohan Anil, Vineet Gupta, Tomer Koren, Kevin Regan, Yoram Singer, 2020 http://arxiv.org/abs/2002.09018 2. Shampoo: Preconditioned Stochastic Tensor Optimization — Vineet Gupta, Tomer Koren, Yoram Singer, 2018 https://scholar.google.com/scholar?q=Shampoo%3A+Preconditioned+Stochastic+Tensor+Optimization 3. Optimizing Neural Networks with Kronecker-factored Approximate Curvature (K-FAC) — James Martens, Roger Grosse, 2015 https://scholar.google.com/scholar?q=Optimizing+Neural+Networks+with+Kronecker-factored+Approximate+Curvature+%28K-FAC%29 4. Adam: A Method for Stochastic Optimization — Diederik P. Kingma, Jimmy Ba, 2014 https://scholar.google.com/scholar?q=Adam%3A+A+Method+for+Stochastic+Optimization 5. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization (AdaGrad) — John Duchi, Elad Hazan, Yoram Singer, 2011 https://scholar.google.com/scholar?q=Adaptive+Subgradient+Methods+for+Online+Learning+and+Stochastic+Optimization+%28AdaGrad%29 6. Optimizing Neural Networks with Kronecker-factored Approximate Curvature — J. Martens, R. Grosse, 2015 https://scholar.google.com/scholar?q=Optimizing+Neural+Networks+with+Kronecker-factored+Approximate+Curvature 7. Distributed Second-Order Optimization using Kronecker-Factored Approximations — J. Ba, J. Martens, R. Grosse, 2017 https://scholar.google.com/scholar?q=Distributed+Second-Order+Optimization+using+Kronecker-Factored+Approximations 8. Large-Scale Distributed Second-Order Optimization Using Kronecker-Factored Approximate Curvature for Deep Convolutional Neural Networks — K. Osawa, Y. Tsuji, Y. Ueno, A. Naruse, R. Yokota, S. Matsuoka, 2019 https://scholar.google.com/scholar?q=Large-Scale+Distributed+Second-Order+Optimization+Using+Kronecker-Factored+Approximate+Curvature+for+Deep+Convolutional+Neural+Networks 9. Large Batch Optimization for Deep Learning: Training BERT in 76 Minutes — Y. You, J. Li, S. Reddi, et al. (LAMB), 2019 https://scholar.google.com/scholar?q=Large+Batch+Optimization+for+Deep+Learning%3A+Training+BERT+in+76+Minutes 10. A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes — Z. Nado, J. Gilmer, C. Shallue, R. Anil, G. Dahl, 2021 https://scholar.google.com/scholar?q=A+Large+Batch+Optimizer+Reality+Check%3A+Traditional%2C+Generic+Optimizers+Suffice+Across+Batch+Sizes 11. Limitations of the Empirical Fisher Approximation for Natural Gradient Descent — F. Kunstner, P. Hennig, L. Balles, 2019 https://scholar.google.com/scholar?q=Limitations+of+the+Empirical+Fisher+Approximation+for+Natural+Gradient+Descent Interactive Visualization: Scalable Second-Order Optimization: Shampoo at Scale

  6. 5 days ago

    Naive Test-Time Adaptation Destabilizes LLM Predictions

    This episode explores a paper proposing SCALENET, a hypernetwork-based fix for unsupervised test-time adaptation (TTA) in large language models. It examines why naive per-prompt gradient updates are unstable — a 70-billion-parameter Llama model's negative log-likelihood balloons from 2.21 to 11.49 after just five adaptation steps — and traces the problem to high-variance single-sample gradients that can't average out the way batch training does. The discussion covers the constrained "adapt-and-reset" setup used in real deployment, where models take a few unsupervised gradient steps on LoRA attention matrices per prompt before discarding the update, and explains why a single global learning rate can't work when small rates do nothing and large ones destroy the model. Listeners interested in the mechanics of on-the-fly model adaptation, LoRA-based efficient tuning, and the control-theory-like challenge of stabilizing per-layer, per-step learning rates will find the breakdown of the failure modes and the proposed hypernetwork solution especially compelling. Sources: 1. Unsupervised Layer-Wise Dynamic Test Time Adaptation for LLMs — Longhuan Xu, Cunjian Chen, Feng Yin, 2026 http://arxiv.org/abs/2602.09719 2. Test-Time Training with Self-Supervision for Generalization under Distribution Shifts — Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei Efros, Moritz Hardt, 2020 https://scholar.google.com/scholar?q=Test-Time+Training+with+Self-Supervision+for+Generalization+under+Distribution+Shifts 3. Tent: Fully Test-Time Adaptation by Entropy Minimization — Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, Trevor Darrell, 2021 https://scholar.google.com/scholar?q=Tent%3A+Fully+Test-Time+Adaptation+by+Entropy+Minimization 4. Test-Time Training on Nearest Neighbors for Large Language Models — Moritz Hardt, Yu Sun, 2024 https://scholar.google.com/scholar?q=Test-Time+Training+on+Nearest+Neighbors+for+Large+Language+Models 5. The Surprising Effectiveness of Test-Time Training for Abstract Reasoning — Ekin Akyürek, Mehul Damani, Linlu Qiu, Han Guo, Yoon Kim, Jacob Andreas, 2024 https://scholar.google.com/scholar?q=The+Surprising+Effectiveness+of+Test-Time+Training+for+Abstract+Reasoning 6. Test-time Learning for Large Language Models — Hu, J., Zhang, Z., Chen, G., Wen, X., Shuai, C., Luo, W., Xiao, B., Li, Y., Tan, M., 2025 https://scholar.google.com/scholar?q=Test-time+Learning+for+Large+Language+Models 7. SLOT: Sample-specific Language Model Optimization at Test-time — Hu, Y., Zhang, X., Fang, X., Chen, Z., Wang, X., Zhang, H., Qi, G., 2025 https://scholar.google.com/scholar?q=SLOT%3A+Sample-specific+Language+Model+Optimization+at+Test-time 8. COME: Test-time Adaption by Conservatively Minimizing Entropy — Zhang, Q., Bian, Y., Kong, X., Zhao, P., Zhang, C., 2024 https://scholar.google.com/scholar?q=COME%3A+Test-time+Adaption+by+Conservatively+Minimizing+Entropy 9. Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models — Rannen-Triki, A., Bornschein, J., Pascanu, R., Hutter, M., et al., 2024 https://scholar.google.com/scholar?q=Revisiting+Dynamic+Evaluation%3A+Online+Adaptation+for+Large+Language+Models 10. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (MAML) — Finn, C., Abbeel, P., Levine, S., 2017 https://scholar.google.com/scholar?q=Model-Agnostic+Meta-Learning+for+Fast+Adaptation+of+Deep+Networks+%28MAML%29 Interactive Visualization: Naive Test-Time Adaptation Destabilizes LLM Predictions

  7. 6 days ago

    Continual Learning in LLMs: Beyond Catastrophic Forgetting

    This episode explores a survey on continual learning in large language models, examining how models can be updated after pretraining without the prohibitive cost of full retraining or the risk of catastrophic forgetting — the phenomenon where new training quietly degrades performance on tasks a model previously handled well. The discussion breaks down the problem across three distinct LLM training stages (pretraining, fine-tuning, and alignment) and maps them onto three classical mitigation strategies: rehearsal-based methods that replay old data, regularization-based methods that penalize changes to critical parameters, and architecture-based methods that add task-specific capacity like adapters or LoRA modules while freezing the rest. The hosts debate the survey's core organizational claim — that structuring the literature by mechanism rather than by application domain (medical, legal, financial) offers a more useful lens for practitioners trying to borrow a specific forgetting-mitigation technique. Listeners interested in the practical tradeoffs of keeping frontier models current — especially around data that can never legally enter a pretraining corpus, like medical or financial records — will find this a grounded framing of a problem every deployed LLM eventually faces. Sources: 1. Continual Learning in Large Language Models: Methods, Challenges, and Opportunities — Hongyang Chen, Zhongwu Sun, Hongfei Ye, Kunchi Li, Xuemin Lin, 2026 http://arxiv.org/abs/2603.12658 2. Don't Stop Pretraining: Adapt Language Models to Domains and Tasks — Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah A. Smith, 2020 https://scholar.google.com/scholar?q=Don%27t+Stop+Pretraining%3A+Adapt+Language+Models+to+Domains+and+Tasks 3. Simple and Scalable Strategies to Continually Pre-train Large Language Models — Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats L. Richter, Quentin Anthony, Timothée Lesort, Eugene Belilovsky, Irina Rish, 2024 https://scholar.google.com/scholar?q=Simple+and+Scalable+Strategies+to+Continually+Pre-train+Large+Language+Models 4. LLaMA Pro: Progressive LLaMA with Block Expansion — Chengyue Wu, Yukang Gan, Yixiao Ge, Zeyu Lu, Jiahao Wang, Ye Feng, Ying Shan, Ping Luo, 2024 https://scholar.google.com/scholar?q=LLaMA+Pro%3A+Progressive+LLaMA+with+Block+Expansion 5. Code Llama: Open Foundation Models for Code — Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, and the Code Llama team at Meta AI, 2023 https://scholar.google.com/scholar?q=Code+Llama%3A+Open+Foundation+Models+for+Code 6. Editing Models with Task Arithmetic — Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, Suchin Gururangan, Ludwig Schmidt, Hannaneh Hajishirzi, Ali Farhadi, 2023 https://scholar.google.com/scholar?q=Editing+Models+with+Task+Arithmetic 7. TIES-Merging: Resolving Interference When Merging Models — Prateek Yadav, Derek Tam, Leshem Choshen, Colin Raffel, Mohit Bansal, 2023 https://scholar.google.com/scholar?q=TIES-Merging%3A+Resolving+Interference+When+Merging+Models 8. Overcoming Catastrophic Forgetting in Neural Networks (EWC) — James Kirkpatrick et al., 2017 https://scholar.google.com/scholar?q=Overcoming+Catastrophic+Forgetting+in+Neural+Networks+%28EWC%29 Interactive Visualization: Continual Learning in LLMs: Beyond Catastrophic Forgetting

  8. 6 days ago

    Hal: Memory-Augmented LLM Agents Still Hit Continual Learning's Wall

    This episode explores a paper examining what happens to continual learning problems when LLM agents shift from parametric updates to memory-augmented architectures. Rather than accepting the industry assumption that external memory sidesteps catastrophic forgetting entirely, the researchers run classic continual-learning protocols on memory-based agents and find the same core problem resurfaces in a new form — shifting from parameter capacity to context-window retrieval capacity. They identify three specific failure modes: retrieval pollution (irrelevant memories crowding the prompt), context competition (useful memories getting displaced by other retrieved items), and memory dilution (relevant material becoming harder to surface as the memory store grows). The discussion traces this argument against the history of catastrophic forgetting and prior mitigation techniques like Elastic Weight Consolidation and Gradient Episodic Memory, then explains how the paper reframes the stability-plasticity dilemma for retrieval-based systems. Listeners interested in agent design, RAG architectures, or the assumptions underlying memory-augmented LLMs will find the paper's reframing — that memory doesn't eliminate the bottleneck, it just relocates it — a useful corrective to a widely repeated industry pitch. Sources: 1. Hal: Memory-Augmented LLM Agents Still Hit Continual Learning's Wall https://arxiv.org/pdf/2604.27003 2. A-Mem: Agentic Memory for LLM Agents — Wujiang Xu, Zujie Liang, Kai Mei, Hang Gao, Juntao Tan, Yongfeng Zhang, 2025 https://scholar.google.com/scholar?q=A-Mem%3A+Agentic+Memory+for+LLM+Agents 3. MemGPT: Towards LLMs as Operating Systems — Charles Packer, Vivian Fang, Shishir G. Patil, Kevin Lin, Sarah Wooders, Joseph E. Gonzalez, 2023 https://scholar.google.com/scholar?q=MemGPT%3A+Towards+LLMs+as+Operating+Systems 4. How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior — Zidi Xiong, Yuping Lin, Wenya Xie, Pengfei He, Zirui Liu, Jiliang Tang, Himabindu Lakkaraju, Zhen Xiang, 2025 https://scholar.google.com/scholar?q=How+Memory+Management+Impacts+LLM+Agents%3A+An+Empirical+Study+of+Experience-Following+Behavior 5. From RAG to Memory: Non-Parametric Continual Learning for Large Language Models — Bernal Jiménez Gutiérrez, Yiheng Shu, Weijian Qi, Sizhe Zhou, Yu Su, 2025 https://scholar.google.com/scholar?q=From+RAG+to+Memory%3A+Non-Parametric+Continual+Learning+for+Large+Language+Models 6. The Probabilistic Relevance Framework: BM25 and Beyond — Stephen Robertson, Hugo Zaragoza, 2009 https://scholar.google.com/scholar?q=The+Probabilistic+Relevance+Framework%3A+BM25+and+Beyond Interactive Visualization: Hal: Memory-Augmented LLM Agents Still Hit Continual Learning's Wall

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AI-generated podcast where hosts Hal Turing and Dr. Ada Shannon discuss the latest research papers and reports in machine learning, AI systems, and optimization. Featuring honest critical analysis, proper citations, and nerdy humor.

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