‘meta-learning’ directory
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Links
- “Meta-Statistical Learning: Supervised Learning of Statistical Inference ”, Peyrard & Cho 2025
- “Competitive Programming With Large Reasoning Models ”, El-Kishky et al 2025
- “Introducing Deep Research: An Agent That Uses Reasoning to Synthesize Large Amounts of Online Information and Complete Multi-Step Research Tasks for You. Available to Pro Users Today, Plus and Team Next ”, OpenAI 2025
- “Where Does In-Context Learning Happen in Large Language Models? ”, Sia et al 2025
- “Metadata Conditioning Accelerates Language Model Pre-Training ”, Gao et al 2025
- “Titans: Learning to Memorize at Test Time ”, Behrouz et al 2024
- “ICLR: In-Context Learning of Representations ”, Park et al 2024
- “Improving Factuality With Explicit Working Memory ”, Chen et al 2024
- “Continuous Autoregressive Models With Noise Augmentation Avoid Error Accumulation ”, Pasini et al 2024
- “State-Space Models Can Learn In-Context by Gradient Descent ”, Sushma et al 2024
- “Thinking LLMs: General Instruction Following With Thought Generation ”, Wu et al 2024
- “MLE-Bench: Evaluating Machine Learning Agents on Machine Learning Engineering ”, Chan et al 2024
- “Contextual Document Embeddings ”, Morris & Rush 2024
- “Generating Diverse and Reliable Features for Few-Shot Learning ”, Xu 2024b
- “When Parts Are Greater Than Sums: Individual LLM Components Can Outperform Full Models ”, Chang et al 2024
- “Probing the Decision Boundaries of In-Context Learning in Large Language Models ”, Zhao et al 2024
- “Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models ”, Denison et al 2024
- “Discovering Preference Optimization Algorithms With and for Large Language Models ”, Lu et al 2024
- “State Soup: In-Context Skill Learning, Retrieval and Mixing ”, Pióro et al 2024
- “Attention As a Hypernetwork ”, Schug et al 2024
- “BERTs Are Generative In-Context Learners ”, Samuel 2024
- “To Believe or Not to Believe Your LLM ”, Yadkori et al 2024
- “Learning to Grok: Emergence of In-Context Learning and Skill Composition in Modular Arithmetic Tasks ”, He et al 2024
- “Auto Evol-Instruct: Automatic Instruction Evolving for Large Language Models ”, Zeng et al 2024
- “A Theoretical Understanding of Self-Correction through In-Context Alignment ”, Wang et al 2024
- “MLPs Learn In-Context ”, Tong & Pehlevan 2024
- “LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language ”, Requeima et al 2024
- “Zero-Shot Tokenizer Transfer ”, Minixhofer et al 2024
- “What Exactly Has TabPFN Learned to Do? ”, McCarter 2024
- “Position: Understanding LLMs Requires More Than Statistical Generalization ”, Reizinger et al 2024
- “SOPHON: Non-Fine-Tunable Learning to Restrain Task Transferability For Pre-Trained Models ”, Deng et al 2024
- “Many-Shot In-Context Learning ”, Agarwal et al 2024
- “Foundational Challenges in Assuring Alignment and Safety of Large Language Models ”, Anwar et al 2024
- “Revisiting the Equivalence of In-Context Learning and Gradient Descent: The Impact of Data Distribution ”, Mahdavi et al 2024
- “Best Practices and Lessons Learned on Synthetic Data for Language Models ”, Liu et al 2024
- “From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples ”, Vacareanu et al 2024
- “Mixture-Of-Depths: Dynamically Allocating Compute in Transformer-Based Language Models ”, Raposo et al 2024
- “Evolutionary Optimization of Model Merging Recipes ”, Akiba et al 2024
- “How Well Can Transformers Emulate In-Context Newton’s Method? ”, Giannou et al 2024
- “Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models ”, Rannen-Triki et al 2024
- “Neural Network Parameter Diffusion ”, Wang et al 2024
- “The Matrix: A Bayesian Learning Model for LLMs ”, Dalal & Misra 2024
- “Rephrasing the Web (WARP): A Recipe for Compute and Data-Efficient Language Modeling ”, Maini et al 2024
- “An Information-Theoretic Analysis of In-Context Learning ”, Jeon et al 2024
- “Deep De Finetti: Recovering Topic Distributions from Large Language Models ”, Zhang et al 2023
- “Generative Multimodal Models Are In-Context Learners ”, Sun et al 2023
- “VILA: On Pre-Training for Visual Language Models ”, Lin et al 2023
- “Evolving Reservoirs for Meta Reinforcement Learning ”, Léger et al 2023
- “The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning ”, Lin et al 2023
- “Learning Few-Shot Imitation As Cultural Transmission ”, Bhoopchand et al 2023
- “In-Context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering ”, Liu et al 2023
- “Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves ”, Deng et al 2023
- “ProSG: Using Prompt Synthetic Gradients to Alleviate Prompt Forgetting of RNN-Like Language Models ”, Luo et al 2023
- “Self-AIXI: Self-Predictive Universal AI ”, Catt et al 2023
- “HyperFields: Towards Zero-Shot Generation of NeRFs from Text ”, Babu et al 2023
- “Transformers Learn Higher-Order Optimization Methods for In-Context Learning: A Study With Linear Models ”, Fu et al 2023
- “Eureka: Human-Level Reward Design via Coding Large Language Models ”, Ma et al 2023
- “How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression? ”, Wu et al 2023
- “Motif: Intrinsic Motivation from Artificial Intelligence Feedback ”, Klissarov et al 2023
- “ExpeL: LLM Agents Are Experiential Learners ”, Zhao et al 2023
- “Diversifying AI: Towards Creative Chess With AlphaZero (AZdb) ”, Zahavy et al 2023
- “RAVEN: In-Context Learning With Retrieval-Augmented Encoder-Decoder Language Models ”, Huang et al 2023
- “CausalLM Is Not Optimal for In-Context Learning ”, Ding et al 2023
- “MetaDiff: Meta-Learning With Conditional Diffusion for Few-Shot Learning ”, Zhang & Yu 2023
- “Self Expanding Neural Networks ”, Mitchell et al 2023
- “Teaching Arithmetic to Small Transformers ”, Lee et al 2023
- “One Step of Gradient Descent Is Provably the Optimal In-Context Learner With One Layer of Linear Self-Attention ”, Mahankali et al 2023
- “Trainable Transformer in Transformer ”, Panigrahi et al 2023
- “Supervised Pretraining Can Learn In-Context Reinforcement Learning ”, Lee et al 2023
- “Pretraining Task Diversity and the Emergence of Non-Bayesian In-Context Learning for Regression ”, Raventós et al 2023
- “Language Models Are Weak Learners ”, Manikandan et al 2023
- “Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks ”, Chevalier-Boisvert et al 2023
- “Improving Long-Horizon Imitation Through Instruction Prediction ”, Hejna et al 2023
- “Schema-Learning and Rebinding As Mechanisms of In-Context Learning and Emergence ”, Swaminathan et al 2023
- “RGD: Stochastic Re-Weighted Gradient Descent via Distributionally Robust Optimization ”, Kumar et al 2023
- “Transformers Learn to Implement Preconditioned Gradient Descent for In-Context Learning ”, Ahn et al 2023
- “Learning Transformer Programs ”, Friedman et al 2023
- “Fundamental Limitations of Alignment in Large Language Models ”, Wolf et al 2023
- “How Well Do Large Language Models Perform in Arithmetic Tasks? ”, Yuan et al 2023
- “Larger Language Models Do In-Context Learning Differently ”, Wei et al 2023
- “BiLD: Big Little Transformer Decoder ”, Kim et al 2023
- “Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery ”, Wen et al 2023
- “Looped Transformers As Programmable Computers ”, Giannou et al 2023
- “A Survey of Meta-Reinforcement Learning ”, Beck et al 2023
- “Human-Like Systematic Generalization through a Meta-Learning Neural Network ”, Lake & Baroni 2023
- “Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent As Meta-Optimizers ”, Dai et al 2022
- “Unnatural Instructions: Tuning Language Models With (Almost) No Human Labor ”, Honovich et al 2022
- “Rethinking the Role of Scale for In-Context Learning: An Interpretability-Based Case Study at 66 Billion Scale ”, Bansal et al 2022
- “Transformers Learn In-Context by Gradient Descent ”, Oswald et al 2022
- “FWL: Meta-Learning Fast Weight Language Models ”, Clark et al 2022
- “What Learning Algorithm Is In-Context Learning? Investigations With Linear Models ”, Akyürek et al 2022
- “Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models ”, Henderson et al 2022
- “VeLO: Training Versatile Learned Optimizers by Scaling Up ”, Metz et al 2022
- “Mysteries of Mode Collapse § Inescapable Wedding Parties ”, Janus 2022
- “BLOOMZ/mT0: Crosslingual Generalization through Multitask Finetuning ”, Muennighoff et al 2022
- “ProMoT: Preserving In-Context Learning Ability in Large Language Model Fine-Tuning ”, Wang et al 2022
- “In-Context Reinforcement Learning With Algorithm Distillation ”, Laskin et al 2022
- “SAP: Bidirectional Language Models Are Also Few-Shot Learners ”, Patel et al 2022
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g.pt: Learning to Learn With Generative Models of Neural Network Checkpoints ”, Peebles et al 2022 - “Simulators ”, Janus 2022
- “AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model ”, Soltan et al 2022
- “Few-Shot Adaptation Works With UnpredicTable Data ”, Chan et al 2022
- “What Can Transformers Learn In-Context? A Case Study of Simple Function Classes ”, Garg et al 2022
- “Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling ”, Nguyen & Grover 2022
- “TabPFN: Meta-Learning a Real-Time Tabular AutoML Method For Small Data ”, Hollmann et al 2022
- “Offline RL Policies Should Be Trained to Be Adaptive ”, Ghosh et al 2022
- “Goal-Conditioned Generators of Deep Policies ”, Faccio et al 2022
- “Prompting Decision Transformer for Few-Shot Policy Generalization ”, Xu et al 2022
- “LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks ”, Dinh et al 2022
- “RHO-LOSS: Prioritized Training on Points That Are Learnable, Worth Learning, and Not Yet Learnt ”, Mindermann et al 2022
- “NOAH: Neural Prompt Search ”, Zhang et al 2022
- “Bongard-HOI: Benchmarking Few-Shot Visual Reasoning for Human-Object Interactions ”, Jiang et al 2022
- “Towards Learning Universal Hyperparameter Optimizers With Transformers ”, Chen et al 2022
- “Instruction Induction: From Few Examples to Natural Language Task Descriptions ”, Honovich et al 2022
- “Gato: A Generalist Agent ”, Reed et al 2022
- “UL2: Unifying Language Learning Paradigms ”, Tay et al 2022
- “Data Distributional Properties Drive Emergent Few-Shot Learning in Transformers ”, Chan et al 2022
- “Tk-Instruct: Benchmarking Generalization via In-Context Instructions on 1,600+ Language Tasks ”, Wang et al 2022
- “What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? ”, Wang et al 2022
- “Effective Mutation Rate Adaptation through Group Elite Selection ”, Kumar et al 2022
- “Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs ”, Akin et al 2022
- “Can Language Models Learn from Explanations in Context? ”, Lampinen et al 2022
- “Auto-Lambda: Disentangling Dynamic Task Relationships ”, Liu et al 2022
- “In-Context Learning and Induction Heads ”, Olsson et al 2022
- “HyperMixer: An MLP-Based Low Cost Alternative to Transformers ”, Mai et al 2022
- “LiteTransformerSearch: Training-Free Neural Architecture Search for Efficient Language Models ”, Javaheripi et al 2022
- “Evolving Curricula With Regret-Based Environment Design ”, Parker-Holder et al 2022
- “HyperPrompt: Prompt-Based Task-Conditioning of Transformers ”, He et al 2022
- “Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? ”, Min et al 2022
- “All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL ”, Arulkumaran et al 2022
- “NeuPL: Neural Population Learning ”, Liu et al 2022
- “Learning Synthetic Environments and Reward Networks for Reinforcement Learning ”, Ferreira et al 2022
- “Datamodels: Predicting Predictions from Training Data ”, Ilyas et al 2022
- “From Data to Functa: Your Data Point Is a Function and You Should Treat It like One ”, Dupont et al 2022
- “Environment Generation for Zero-Shot Compositional Reinforcement Learning ”, Gur et al 2022
- “Active Predictive Coding Networks: A Neural Solution to the Problem of Learning Reference Frames and Part-Whole Hierarchies ”, Gklezakos & Rao 2022
- “Learning Robust Perceptive Locomotion for Quadrupedal Robots in the Wild ”, Miki et al 2022
- “Automated Reinforcement Learning (AutoRL): A Survey and Open Problems ”, Parker-Holder et al 2022
- “In Defense of the Unitary Scalarization for Deep Multi-Task Learning ”, Kurin et al 2022
- “HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning ”, Zhmoginov et al 2022
- “Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning ”, Curry et al 2022
- “The Curse of Zero Task Diversity: On the Failure of Transfer Learning to Outperform MAML and Their Empirical Equivalence ”, Miranda et al 2021
- “A Mathematical Framework for Transformer Circuits ”, Elhage et al 2021
- “PFNs: Transformers Can Do Bayesian Inference ”, Müller et al 2021
- “How to Learn and Represent Abstractions: An Investigation Using Symbolic Alchemy ”, AlKhamissi et al 2021
- “Noether Networks: Meta-Learning Useful Conserved Quantities ”, Alet et al 2021
- “A Rational Reinterpretation of Dual-Process Theories ”, Milli et al 2021
- “A General Language Assistant As a Laboratory for Alignment ”, Askell et al 2021
- “A Modern Self-Referential Weight Matrix That Learns to Modify Itself ”, Irie et al 2021
- “A Survey of Generalization in Deep Reinforcement Learning ”, Kirk et al 2021
- “Gradients Are Not All You Need ”, Metz et al 2021
- “An Explanation of In-Context Learning As Implicit Bayesian Inference ”, Xie et al 2021
- “Procedural Generalization by Planning With Self-Supervised World Models ”, Anand et al 2021
- “MetaICL: Learning to Learn In Context ”, Min et al 2021
- “Logical Activation Functions: Logit-Space Equivalents of Probabilistic Boolean Operators ”, Lowe et al 2021
- “Shaking the Foundations: Delusions in Sequence Models for Interaction and Control ”, Ortega et al 2021
- “Meta-Learning, Social Cognition and Consciousness in Brains and Machines ”, Langdon et al 2021
- “T0: Multitask Prompted Training Enables Zero-Shot Task Generalization ”, Sanh et al 2021
- “Replay-Guided Adversarial Environment Design ”, Jiang et al 2021
- “Embodied Intelligence via Learning and Evolution ”, Gupta et al 2021
- “Transformers Are Meta-Reinforcement Learners ”, Anonymous 2021
- “Scalable Online Planning via Reinforcement Learning Fine-Tuning ”, Fickinger et al 2021
- “Dropout’s Dream Land: Generalization from Learned Simulators to Reality ”, Wellmer & Kwok 2021
- “Is Curiosity All You Need? On the Utility of Emergent Behaviors from Curious Exploration ”, Groth et al 2021
- “Bootstrapped Meta-Learning ”, Flennerhag et al 2021
- “The Sensory Neuron As a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning ”, Tang & Ha 2021
- “FLAN: Finetuned Language Models Are Zero-Shot Learners ”, Wei et al 2021
- “The AI Economist: Optimal Economic Policy Design via Two-Level Deep Reinforcement Learning ”, Zheng et al 2021
- “Open-Ended Learning Leads to Generally Capable Agents ”, Team et al 2021
- “Dataset Distillation With Infinitely Wide Convolutional Networks ”, Nguyen et al 2021
- “Why Generalization in RL Is Difficult: Epistemic POMDPs and Implicit Partial Observability ”, Ghosh et al 2021
- “PonderNet: Learning to Ponder ”, Banino et al 2021
- “Multimodal Few-Shot Learning With Frozen Language Models ”, Tsimpoukelli et al 2021
- “LHOPT: A Generalizable Approach to Learning Optimizers ”, Almeida et al 2021
- “Towards Mental Time Travel: a Hierarchical Memory for Reinforcement Learning Agents ”, Lampinen et al 2021
- “A Full-Stack Accelerator Search Technique for Vision Applications ”, Zhang et al 2021
- “Reward Is Enough ”, Silver et al 2021
- “Bayesian Optimization Is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020 ”, Turner et al 2021
- “CrossFit: A Few-Shot Learning Challenge for Cross-Task Generalization in NLP ”, Ye et al 2021
- “Podracer Architectures for Scalable Reinforcement Learning ”, Hessel et al 2021
- “BLUR: Meta-Learning Bidirectional Update Rules ”, Sandler et al 2021
- “Asymmetric Self-Play for Automatic Goal Discovery in Robotic Manipulation ”, OpenAI et al 2021
- “OmniNet: Omnidirectional Representations from Transformers ”, Tay et al 2021
- “Linear Transformers Are Secretly Fast Weight Programmers ”, Schlag et al 2021
- “Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm ”, Reynolds & McDonell 2021
- “ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution ”, Song et al 2021
- “Training Learned Optimizers With Randomly Initialized Learned Optimizers ”, Metz et al 2021
- “Evolving Reinforcement Learning Algorithms ”, Co-Reyes et al 2021
- “Meta Pseudo Labels ”, Pham et al 2021
- “Meta Learning Backpropagation And Improving It ”, Kirsch & Schmidhuber 2020
- “Emergent Complexity and Zero-Shot Transfer via Unsupervised Environment Design ”, Dennis et al 2020
- “Scaling down Deep Learning ”, Greydanus 2020
- “Reverse Engineering Learned Optimizers Reveals Known and Novel Mechanisms ”, Maheswaranathan et al 2020
- “Dataset Meta-Learning from Kernel Ridge-Regression ”, Nguyen et al 2020
- “MELD: Meta-Reinforcement Learning from Images via Latent State Models ”, Zhao et al 2020
- “Meta-Trained Agents Implement Bayes-Optimal Agents ”, Mikulik et al 2020
- “Learning Not to Learn: Nature versus Nurture in Silico ”, Lange & Sprekeler 2020
- “Prioritized Level Replay ”, Jiang et al 2020
- “Tasks, Stability, Architecture, and Compute: Training More Effective Learned Optimizers, and Using Them to Train Themselves ”, Metz et al 2020
- “Hidden Incentives for Auto-Induced Distributional Shift ”, Krueger et al 2020
- “Grounded Language Learning Fast and Slow ”, Hill et al 2020
- “Matt Botvinick on the Spontaneous Emergence of Learning Algorithms ”, Scholl 2020
- “Discovering Reinforcement Learning Algorithms ”, Oh et al 2020
- “Deep Reinforcement Learning and Its Neuroscientific Implications ”, Botvinick 2020
- “Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions ”, Chang et al 2020
- “Rapid Task-Solving in Novel Environments ”, Ritter et al 2020
- “FBNetV3: Joint Architecture-Recipe Search Using Predictor Pretraining ”, Dai et al 2020
- “GPT-3: Language Models Are Few-Shot Learners ”, Brown et al 2020
- “Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search ”, Rawal et al 2020
- “Automatic Discovery of Interpretable Planning Strategies ”, Skirzyński et al 2020
- “Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks ”, Schoettler et al 2020
- “A Comparison of Methods for Treatment Assignment With an Application to Playlist Generation ”, Fernández-Loría et al 2020
- “Approximate Exploitability: Learning a Best Response in Large Games ”, Timbers et al 2020
- “Meta-Learning in Neural Networks: A Survey ”, Hospedales et al 2020
- “Agent57: Outperforming the Atari Human Benchmark ”, Badia et al 2020
- “Designing Network Design Spaces ”, Radosavovic et al 2020
- “Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and Their Solutions ”, Wang et al 2020
- “Accelerating and Improving AlphaZero Using Population Based Training ”, Wu et al 2020
- “Meta-Learning Curiosity Algorithms ”, Alet et al 2020
- “AutoML-Zero: Evolving Machine Learning Algorithms From Scratch ”, Real et al 2020
- “AutoML-Zero: Open Source Code for the Paper: "AutoML-Zero: Evolving Machine Learning Algorithms From Scratch" ”, Real et al 2020
- “Effective Diversity in Population Based Reinforcement Learning ”, Parker-Holder et al 2020
- “AI Helps Warehouse Robots Pick Up New Tricks: Backed by Machine Learning Luminaries, Covariant.ai’s Bots Can Handle Jobs Previously Needing a Human Touch ”, Knight 2020
- “Smooth Markets: A Basic Mechanism for Organizing Gradient-Based Learners ”, Balduzzi et al 2020
- “AutoML-Zero: Evolving Code That Learns ”, Real & Liang 2020
- “Learning Neural Activations ”, Minhas & Asif 2019
- “Meta-Learning without Memorization ”, Yin et al 2019
- “MetaFun: Meta-Learning With Iterative Functional Updates ”, Xu et al 2019
- “Leveraging Procedural Generation to Benchmark Reinforcement Learning ”, Cobbe et al 2019
- “Procgen Benchmark: We’re Releasing Procgen Benchmark, 16 Simple-To-Use Procedurally-Generated Environments Which Provide a Direct Measure of How Quickly a Reinforcement Learning Agent Learns Generalizable Skills ”, Cobbe et al 2019
- “Increasing Generality in Machine Learning through Procedural Content Generation ”, Risi & Togelius 2019
- “SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning ”, Wang et al 2019
- “Optimizing Millions of Hyperparameters by Implicit Differentiation ”, Lorraine et al 2019
- “Learning to Predict Without Looking Ahead: World Models Without Forward Prediction ”, Freeman et al 2019
- “Learning to Predict Without Looking Ahead: World Models Without Forward Prediction [Blog] ”, Freeman et al 2019
- “Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning ”, Yu et al 2019
- “Solving Rubik’s Cube With a Robot Hand ”, OpenAI et al 2019
- “Solving Rubik’s Cube With a Robot Hand [Blog] ”, OpenAI 2019
- “Gradient Descent: The Ultimate Optimizer ”, Chandra et al 2019
- “Data Valuation Using Reinforcement Learning ”, Yoon et al 2019
- “Multiplicative Interactions and Where to Find Them ”, Jayakumar et al 2019
- “ANIL: Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML ”, Raghu et al 2019
- “Emergent Tool Use From Multi-Agent Autocurricula ”, Baker et al 2019
- “Meta-Learning With Implicit Gradients ”, Rajeswaran et al 2019
- “A Critique of Pure Learning and What Artificial Neural Networks Can Learn from Animal Brains ”, Zador 2019
- “AutoML: A Survey of the State-Of-The-Art ”, He et al 2019
- “Metalearned Neural Memory ”, Munkhdalai et al 2019
- “Algorithms for Hyper-Parameter Optimization ”, Bergstra et al 2019
- “Evolving the Hearthstone Meta ”, Silva et al 2019
- “Meta Reinforcement Learning ”, Weng 2019
- “One Epoch Is All You Need ”, Komatsuzaki 2019
- “Compositional Generalization through Meta Sequence-To-Sequence Learning ”, Lake 2019
- “Risks from Learned Optimization in Advanced Machine Learning Systems ”, Hubinger et al 2019
- “ICML 2019 Notes ”, Abel 2019
- “SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers ”, Fedorov et al 2019
- “AI-GAs: AI-Generating Algorithms, an Alternate Paradigm for Producing General Artificial Intelligence ”, Clune 2019
- “Alpha MAML: Adaptive Model-Agnostic Meta-Learning ”, Behl et al 2019
- “Reinforcement Learning, Fast and Slow ”, Botvinick et al 2019
- “Meta Reinforcement Learning As Task Inference ”, Humplik et al 2019
- “Learning Loss for Active Learning ”, Yoo & Kweon 2019
- “Meta-Learning of Sequential Strategies ”, Ortega et al 2019
- “Searching for MobileNetV3 ”, Howard et al 2019
- “Meta-Learners’ Learning Dynamics Are unlike Learners’ ”, Rabinowitz 2019
- “Ray Interference: a Source of Plateaus in Deep Reinforcement Learning ”, Schaul et al 2019
- “AlphaX: EXploring Neural Architectures With Deep Neural Networks and Monte Carlo Tree Search ”, Wang et al 2019
- “Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables ”, Rakelly et al 2019
- “Task2Vec: Task Embedding for Meta-Learning ”, Achille et al 2019
- “The Omniglot Challenge: a 3-Year Progress Report ”, Lake et al 2019
- “FIGR: Few-Shot Image Generation With Reptile ”, Clouâtre & Demers 2019
- “Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions ”, Wang et al 2019
- “Meta-Learning Neural Bloom Filters ”, Rae 2019
- “Malthusian Reinforcement Learning ”, Leibo et al 2018
- “Quantifying Generalization in Reinforcement Learning ”, Cobbe et al 2018
- “An Introduction to Deep Reinforcement Learning ”, Francois-Lavet et al 2018
- “Meta-Learning: Learning to Learn Fast ”, Weng 2018
- “Evolving Space-Time Neural Architectures for Videos ”, Piergiovanni et al 2018
- “Understanding and Correcting Pathologies in the Training of Learned Optimizers ”, Metz et al 2018
- “BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning ”, Chevalier-Boisvert et al 2018
- “Deep Reinforcement Learning ”, Li 2018
- “Searching for Efficient Multi-Scale Architectures for Dense Image Prediction ”, Chen et al 2018
- “Backprop Evolution ”, Alber et al 2018
- “Learning Dexterous In-Hand Manipulation ”, OpenAI et al 2018
- “LEO: Meta-Learning With Latent Embedding Optimization ”, Rusu et al 2018
- “Automatically Composing Representation Transformations As a Means for Generalization ”, Chang et al 2018
- “Human-Level Performance in First-Person Multiplayer Games With Population-Based Deep Reinforcement Learning ”, Jaderberg et al 2018
- “Guided Evolutionary Strategies: Augmenting Random Search With Surrogate Gradients ”, Maheswaranathan et al 2018
- “RUDDER: Return Decomposition for Delayed Rewards ”, Arjona-Medina et al 2018
- “Meta-Learning Transferable Active Learning Policies by Deep Reinforcement Learning ”, Pang et al 2018
- “Fingerprint Policy Optimization for Robust Reinforcement Learning ”, Paul et al 2018
- “AutoAugment: Learning Augmentation Policies from Data ”, Cubuk et al 2018
- “Meta-Gradient Reinforcement Learning ”, Xu et al 2018
- “Continuous Learning in a Hierarchical Multiscale Neural Network ”, Wolf et al 2018
- “Prefrontal Cortex As a Meta-Reinforcement Learning System ”, Wang et al 2018
- “Meta-Learning Update Rules for Unsupervised Representation Learning ”, Metz et al 2018
- “Reviving and Improving Recurrent Back-Propagation ”, Liao et al 2018
- “Kickstarting Deep Reinforcement Learning ”, Schmitt et al 2018
- “Reptile: On First-Order Meta-Learning Algorithms ”, Nichol et al 2018
- “Some Considerations on Learning to Explore via Meta-Reinforcement Learning ”, Stadie et al 2018
- “One Big Net For Everything ”, Schmidhuber 2018
- “Machine Theory of Mind ”, Rabinowitz et al 2018
- “Evolved Policy Gradients ”, Houthooft et al 2018
- “One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning ”, Yu et al 2018
- “Rover Descent: Learning to Optimize by Learning to Navigate on Prototypical Loss Surfaces ”, Faury & Vasile 2018
- “ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks ”, Kim & Choi 2018
- “Population Based Training of Neural Networks ”, Jaderberg et al 2017
- “BlockDrop: Dynamic Inference Paths in Residual Networks ”, Wu et al 2017
- “Learning to Select Computations ”, Callaway et al 2017
- “Learning to Generalize: Meta-Learning for Domain Generalization ”, Li et al 2017
- “Efficient K-Shot Learning With Regularized Deep Networks ”, Yoo et al 2017
- “Online Learning of a Memory for Learning Rates ”, Meier et al 2017
- “One-Shot Visual Imitation Learning via Meta-Learning ”, Finn et al 2017
- “Supervising Unsupervised Learning ”, Garg & Kalai 2017
- “Learning With Opponent-Learning Awareness ”, Foerster et al 2017
- “SMASH: One-Shot Model Architecture Search through HyperNetworks ”, Brock et al 2017
- “Stochastic Optimization With Bandit Sampling ”, Salehi et al 2017
- “A Simple Neural Attentive Meta-Learner ”, Mishra et al 2017
- “Reinforcement Learning for Learning Rate Control ”, Xu et al 2017
- “Metacontrol for Adaptive Imagination-Based Optimization ”, Hamrick et al 2017
- “Deciding How to Decide: Dynamic Routing in Artificial Neural Networks ”, McGill & Perona 2017
- “Prototypical Networks for Few-Shot Learning ”, Snell et al 2017
- “Learned Optimizers That Scale and Generalize ”, Wichrowska et al 2017
- “MAML: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks ”, Finn et al 2017
- “Learning to Optimize Neural Nets ”, Li & Malik 2017
- “Understanding Synthetic Gradients and Decoupled Neural Interfaces ”, Czarnecki et al 2017
- “Optimization As a Model for Few-Shot Learning ”, Ravi & Larochelle 2017
- “Learning to Superoptimize Programs ”, Bunel et al 2017
- “Discovering Objects and Their Relations from Entangled Scene Representations ”, Raposo et al 2017
- “Google Vizier: A Service for Black-Box Optimization ”, Golovin 2017
- “An Actor-Critic Algorithm for Learning Rate Learning ”, Xu et al 2016
- “A Bird’s Eye View of Synthetic Gradients ”, Greydanus 2016
- “Learning to Reinforcement Learn ”, Wang et al 2016
- “Learning to Learn without Gradient Descent by Gradient Descent ”, Chen et al 2016
- “RL2: Fast Reinforcement Learning via Slow Reinforcement Learning ”, Duan et al 2016
- “Designing Neural Network Architectures Using Reinforcement Learning ”, Baker et al 2016
- “Using Fast Weights to Attend to the Recent Past ”, Ba et al 2016
- “HyperNetworks ”, Ha et al 2016
- “Decoupled Neural Interfaces Using Synthetic Gradients ”, Jaderberg et al 2016
- “Learning to Learn by Gradient Descent by Gradient Descent ”, Andrychowicz et al 2016
- “Matching Networks for One Shot Learning ”, Vinyals et al 2016
- “Learning to Optimize ”, Li & Malik 2016
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- “Machine Teaching: an Inverse Problem to Machine Learning and an Approach Toward Optimal Education ”, Zhu 2015b
- “Human-Level Concept Learning through Probabilistic Program Induction ”, Lake 2015
- “Robots That Can Adapt like Animals ”, Cully et al 2014
- “Deep Learning in Neural Networks: An Overview ”, Schmidhuber 2014
- “Practical Bayesian Optimization of Machine Learning Algorithms ”, Snoek et al 2012
- “Evolutionary Importance of Phenotypic Accommodation in Novel Environments: an Empirical Test of the Baldwin Effect ”, Badyaev 2009
- “Optimal Ordered Problem Solver (OOPS) ”, Schmidhuber 2002
- “Learning to Learn Using Gradient Descent ”, Hochreiter et al 2001
- “On the Optimization of a Synaptic Learning Rule ”, Bengio et al 1997
- “Interactions between Learning and Evolution ”, Ackley & Littman 1992
- “Learning to Control Fast-Weight Memories: An Alternative to Dynamic Recurrent Networks ”, Schmidhuber 1992b
- “Learning a Synaptic Learning Rule ”, Bengio et al 1991
- “Reinforcement Learning: An Introduction § Designing Reward Signals ”, Sutton & Barto 2025 (page 491)
- “Exploring Hyperparameter Meta-Loss Landscapes With Jax ”
- “Metalearning ”
- “Universal Search § OOPS and Other Incremental Variations ”
- “Extrapolating to Unnatural Language Processing With GPT-3’s In-Context Learning: The Good, the Bad, and the Mysterious ”
- “How Does In-Context Learning Work? A Framework for Understanding the Differences from Traditional Supervised Learning ”
- “Rapid Motor Adaptation for Legged Robots ”
- “Collaborating With Humans Requires Understanding Them ”
- “Why Generalization in RL Is Difficult: Epistemic POMDPs and Implicit Partial Observability [Blog] ”
- “Hypernetworks [Blog] ”, Ha 2025
- “Action and Perception As Divergence Minimization ”
- “AlphaStar: Mastering the Real-Time Strategy Game StarCraft II ”
- “Prefrontal Cortex As a Meta-Reinforcement Learning System [Blog] ”
- “The Lie Comes First, the Worlds to Accommodate It ”
- “Sgdstore/experiments/omniglot at Master ”
- “Curriculum For Reinforcement Learning ”
- “Neural Architecture Search ”
- “MetaGenRL: Improving Generalization in Meta Reinforcement Learning ”
- “The RetroInstruct Guide To Synthetic Text Data ”, Pressman 2025
- “2022: 25-Year Anniversary: LSTM (1997), All Computable Metaverses, Hierarchical Q-Learning, Adversarial Intrinsic Reinforcement Learning, Low-Complexity NNs, Low-Complexity Art, Meta-RL, Soccer Learning ”
- “Metalearning or Learning to Learn Since 1987 ”
- “The Future of Artificial Intelligence Is Self-Organizing and Self-Assembling ”
- “Domain-Adaptive Meta-Learning ”
- “How to Fix Reinforcement Learning ”
- “Introducing Adept ”
- “Optimal Learning: Computational Procedures for Bayes-Adaptive Markov Decision Processes ”
- “Risks from Learned Optimization: Introduction ”
- “How Good Are LLMs at Doing ML on an Unknown Dataset? ”
- “Early Situational Awareness and Its Implications, a Story ”
- “AI Is Learning How to Create Itself ”
- “Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind ”
- “SMASH: One-Shot Model Architecture Search through HyperNetworks [Video] ”
- “Solving Rubik’s Cube With a Robot Hand: Perturbations ”
- “WELM ”
- Wikipedia
- Miscellaneous
- Bibliography
See Also
Gwern
“Free-Play Periods for RL Agents ”, Gwern 2023
“WBE & DRL: a Middle Way of Imitation Learning on Brains ”, Gwern 2018
Links
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Meta-Statistical Learning: Supervised Learning of Statistical Inference
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“Where Does In-Context Learning Happen in Large Language Models? ”, Sia et al 2025
Where does In-context Learning Happen in Large Language Models?
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Metadata Conditioning Accelerates Language Model Pre-training
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Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves
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HyperFields: Towards Zero-Shot Generation of NeRFs from Text
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Eureka: Human-Level Reward Design via Coding Large Language Models
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How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?
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Motif: Intrinsic Motivation from Artificial Intelligence Feedback
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Diversifying AI: Towards Creative Chess with AlphaZero (AZdb)
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RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models
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MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning
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Supervised Pretraining Can Learn In-Context Reinforcement Learning
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Improving Long-Horizon Imitation Through Instruction Prediction
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RGD: Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization
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Transformers learn to implement preconditioned gradient descent for in-context learning
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Fundamental Limitations of Alignment in Large Language Models
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How well do Large Language Models perform in Arithmetic tasks?
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Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery
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Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers
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Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor
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BLOOMZ/mT0: Crosslingual Generalization through Multitask Finetuning
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ProMoT: Preserving In-Context Learning ability in Large Language Model Fine-tuning
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In-context Reinforcement Learning with Algorithm Distillation
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g.pt: Learning to Learn with Generative Models of Neural Network Checkpoints
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AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model
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TabPFN: Meta-Learning a Real-Time Tabular AutoML Method For Small Data
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LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks
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RHO-LOSS: Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt
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Towards Learning Universal Hyperparameter Optimizers with Transformers
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HyperMixer: An MLP-based Low Cost Alternative to Transformers
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LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models
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From data to functa: Your data point is a function and you should treat it like one
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Environment Generation for Zero-Shot Compositional Reinforcement Learning
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Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
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In Defense of the Unitary Scalarization for Deep Multi-Task Learning
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HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
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Finding General Equilibria in Many-Agent Economic Simulations Using Deep Reinforcement Learning
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An Explanation of In-context Learning as Implicit Bayesian Inference
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Procedural Generalization by Planning with Self-Supervised World Models
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Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators
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Shaking the foundations: delusions in sequence models for interaction and control
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Meta-learning, social cognition and consciousness in brains and machines
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Scalable Online Planning via Reinforcement Learning Fine-Tuning
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Dropout’s Dream Land: Generalization from Learned Simulators to Reality
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Is Curiosity All You Need? On the Utility of Emergent Behaviors from Curious Exploration
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The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning
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Dataset Distillation with Infinitely Wide Convolutional Networks
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Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability
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Towards mental time travel: a hierarchical memory for reinforcement learning agents
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A Full-stack Accelerator Search Technique for Vision Applications
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CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP
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Asymmetric self-play for automatic goal discovery in robotic manipulation
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“Linear Transformers Are Secretly Fast Weight Programmers ”, Schlag et al 2021
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Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm
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ES-ENAS: Blackbox Optimization over Hybrid Spaces via Combinatorial and Continuous Evolution
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Training Learned Optimizers with Randomly Initialized Learned Optimizers
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“Meta Pseudo Labels ”, Pham et al 2021
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Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
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“Deep Reinforcement Learning and Its Neuroscientific Implications ”, Botvinick 2020
Deep Reinforcement Learning and Its Neuroscientific Implications
“Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions ”, Chang et al 2020
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“Rapid Task-Solving in Novel Environments ”, Ritter et al 2020
“FBNetV3: Joint Architecture-Recipe Search Using Predictor Pretraining ”, Dai et al 2020
FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining
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“Synthetic Petri Dish: A Novel Surrogate Model for Rapid Architecture Search ”, Rawal et al 2020
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“Automatic Discovery of Interpretable Planning Strategies ”, Skirzyński et al 2020
“Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks ”, Schoettler et al 2020
Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks
“A Comparison of Methods for Treatment Assignment With an Application to Playlist Generation ”, Fernández-Loría et al 2020
A Comparison of Methods for Treatment Assignment with an Application to Playlist Generation
“Approximate Exploitability: Learning a Best Response in Large Games ”, Timbers et al 2020
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“AI Helps Warehouse Robots Pick Up New Tricks: Backed by Machine Learning Luminaries, Covariant.ai’s Bots Can Handle Jobs Previously Needing a Human Touch ”, Knight 2020
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A Bird’s Eye View of Synthetic Gradients :
View External Link:
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Reinforcement Learning: An Introduction § Designing Reward Signals :
“Exploring Hyperparameter Meta-Loss Landscapes With Jax ”
“Metalearning ”
“Universal Search § OOPS and Other Incremental Variations ”
“Extrapolating to Unnatural Language Processing With GPT-3’s In-Context Learning: The Good, the Bad, and the Mysterious ”
“How Does In-Context Learning Work? A Framework for Understanding the Differences from Traditional Supervised Learning ”
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Rapid Motor Adaptation for Legged Robots :
View External Link:
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View External Link:
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Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability [blog]
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“Action and Perception As Divergence Minimization ”
Action and Perception as Divergence Minimization :
View External Link:
“AlphaStar: Mastering the Real-Time Strategy Game StarCraft II ”
AlphaStar: Mastering the Real-Time Strategy Game StarCraft II
“Prefrontal Cortex As a Meta-Reinforcement Learning System [Blog] ”
Prefrontal cortex as a meta-reinforcement learning system [blog] :
“The Lie Comes First, the Worlds to Accommodate It ”
“Sgdstore/experiments/omniglot at Master ”
“Curriculum For Reinforcement Learning ”
“Neural Architecture Search ”
“MetaGenRL: Improving Generalization in Meta Reinforcement Learning ”
MetaGenRL: Improving Generalization in Meta Reinforcement Learning :
“The RetroInstruct Guide To Synthetic Text Data ”, Pressman 2025
“2022: 25-Year Anniversary: LSTM (1997), All Computable Metaverses, Hierarchical Q-Learning, Adversarial Intrinsic Reinforcement Learning, Low-Complexity NNs, Low-Complexity Art, Meta-RL, Soccer Learning ”
“Metalearning or Learning to Learn Since 1987 ”
“The Future of Artificial Intelligence Is Self-Organizing and Self-Assembling ”
The Future of Artificial Intelligence is Self-Organizing and Self-Assembling
“Domain-Adaptive Meta-Learning ”
“How to Fix Reinforcement Learning ”
“Introducing Adept ”
“Optimal Learning: Computational Procedures for Bayes-Adaptive Markov Decision Processes ”
Optimal Learning: Computational Procedures for Bayes-Adaptive Markov Decision Processes
“Risks from Learned Optimization: Introduction ”
“How Good Are LLMs at Doing ML on an Unknown Dataset? ”
“Early Situational Awareness and Its Implications, a Story ”
“AI Is Learning How to Create Itself ”
“Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind ”
Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind :
“SMASH: One-Shot Model Architecture Search through HyperNetworks [Video] ”
SMASH: One-Shot Model Architecture Search through HyperNetworks [video] :
“Solving Rubik’s Cube With a Robot Hand: Perturbations ”
“WELM ”
Wikipedia
Miscellaneous
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/doc/reinforcement-learning/meta-learning/2025-elkishky-figure6-o3learnedselftestingstrategy.jpg: -
https://ai.meta.com/blog/harmful-content-can-evolve-quickly-our-new-ai-system-adapts-to-tackle-it -
https://ai.meta.com/blog/harmful-content-can-evolve-quickly-our-new-ai-system-adapts-to-tackle-it/: -
https://blog.waymo.com/2020/04/using-automated-data-augmentation-to.html#google: -
https://pages.ucsd.edu/~rbelew/courses/cogs184_w10/readings/HintonNowlan97.pdf: -
https://research.google/blog/permutation-invariant-neural-networks-for-reinforcement-learning/ -
https://www.lesswrong.com/posts/bC5xd7wQCnTDw7Kyx/getting-up-to-speed-on-the-speed-prior-in-2022: -
https://www.lesswrong.com/posts/sY3a4Rfa48CgteBEm/chatgpt-can-learn-indirect-control: -
https://www.quantamagazine.org/researchers-build-ai-that-builds-ai-20220125/
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