arXiv AI

MetaLearnNCA: Few-Shot Offline Meta-Learning via Interacting Neural Cellular Automata

MetaLearnNCA introduces a decentralized few‑shot meta‑learning framework that uses interacting Neural Cellular Automata (NCAs) to adapt to new tasks without backpropagation. The method splits adaptation into an Active‑NCA that performs task inference on a 2D spatial memory grid, and a learned Meta‑NCA that diffuses error residuals locally to update this grid. Experiments show competitive in‑distribution performance (96.12% on Omniglot) and superior out‑of‑distribution transfer on MNIST, KMNIST, and Fashion‑MNIST compared to canonical meta‑learners.

arXiv AI

Sep 21

Neural Cellular Automata Learn General Features in their Hidden Channels

Neural Cellular Automata (NCAs) are shown to learn general, scale‑invariant topological primitives in their hidden channels, which can be transferred from a teacher to a student model for few‑shot learning. The study introduces a transfer‑learning mechanism that injects pretrained hidden states into a student, improving early optimization and outperforming recurrent and feed‑forward baselines on MNIST benchmarks with only ~9,800 parameters. Mechanistic analysis reveals that hidden channels decouple feature extraction from classification, converging to mutually orthogonal states that absorb morphological complexity.

By Etienne Guichard, Stefano Nichele

fine-tuning benchmarks

arXiv AI

Jul 10

Architecture Generalization with MetaNCA

arXiv:2607. 07743v1 Announce Type: cross Abstract: Self-organization is an emergent property of life, driven by the collective behavior of individual components acting on local information.

By Meet Barot, Daniel Berenberg, Sina Khajehabdollahi

llms

arXiv AI

Jun 15

Unsupervised Learning of Efficient Exploration: Pre-training Adaptive Policies via Self-Imposed Goals

arXiv:2601. 19810v2 Announce Type: replace-cross Abstract: Unsupervised pre-training can equip reinforcement learning agents with prior knowledge and accelerate learning in downstream tasks.

By Octavio Pappalardo

agents reinforcement-learning fine-tuning benchmarks safety

Hugging Face Trending Papers

Jul 7

Efficient Long-Horizon Learning for Learned Optimization

Learned optimization aims to improve upon hand-designed optimizers (e. g.

llms computer-vision

arXiv Machine Learning

Jul 1

Diffusing Blame: Task-Dependent Credit Assignment in Biologically Plausible Dual-Stream Networks

arXiv:2606. 31700v1 Announce Type: new Abstract: Biological neural circuits obey Dale's principle: each neuron's synapses are uniformly excitatory or inhibitory.

By Yutaro Yamada, Luca Grillotti, Rujikorn Charakorn, Sebastian Risi, David Ha, Robert Tjarko Lange

diffusion reinforcement-learning benchmarks safety

arXiv Machine Learning

Sep 25

Online Task Adaptation via Self-Organisation

The paper proposes a method for task adaptation that eliminates the need for gradient computation during adaptation. Using a Neural Cellular Automaton, the authors train recurrent dynamics and memory read/write operations via backpropagation, then fix the slow model parameters. Online adaptation is achieved solely through local memory updates driven by prediction errors, enabling significant performance gains on new classification tasks with a single support set pass.

By Krsto Prorokovi\'c

arXiv Machine Learning

Jul 9

Efficient Long-Horizon Learning for Learned Optimization

arXiv:2607. 06772v1 Announce Type: new Abstract: Learned optimization aims to improve upon hand-designed optimizers (e.

By Xiaolong Huang, Benjamin Th\'erien, James Harrison, Eugene Belilovsky

llms computer-vision

arXiv AI

4d ago

Learning Computational Structure for Sample-Efficient Prediction

arXiv:2610.28555v1 Announce Type: cross Abstract: Statistical learning in high dimensions suffers from the curse of dimensionality. Fortunately, many functions of practical interest are compositional...

By Hangyu Zhou, Constantine Dovrolis, Stephen Mussmann

rag robotics efficiency benchmarks

arXiv Machine Learning

Aug 18

Metaplasticity as adaptive gradient preconditioning for incremental learning

arXiv:2608. 14634v1 Announce Type: new Abstract: Biological intelligence naturally prevents catastrophic forgetting through Complementary Learning Systems (CLS) theory, a macroscopic consolidation process driven at the local level by synaptic metaplasticity: the continuous, history-dependent neuromodulation of individual synapses.

By Isabelle Aguilar, Zayn Andre Zainal, Omid Kavehei

benchmarks

arXiv AI

Jul 22

From Trajectories to Instructions: Language-Conditioned Meta-Reinforcement Learning

arXiv:2607. 18830v1 Announce Type: cross Abstract: Model-Agnostic Meta-Learning (MAML) is a widely used framework for reinforcement learning (RL) that enables efficient transfer by learning global policy parameters that can be rapidly adapted to new tasks.

By Garvit Singla, Uma Maheswari Natarajan, Raghuram Bharadwaj Diddigi

rag reinforcement-learning benchmarks

arXiv Machine Learning

Sep 22

Personalized Federated Reinforcement Learning via Model-Agnostic Meta-Learning: Convergence of Exact and Hessian-Free Meta-Policy Gradients

arXiv:2609. 22833v1 Announce Type: new Abstract: We study personalized federated reinforcement learning, in which $n$ agents, each acting in its own Markov decision process, collaborate through a server to learn a shared MAML-style policy initialization that becomes effective for an individual agent once that agent adapts it with a single local policy-gradient step.

By Ali Beikmohammadi, Sarit Khirirat, Sindri Magn\'usson

agents reinforcement-learning safety

arXiv AI

Oct 15

Pessimistic Latent Task-aware Optimization for Robust Offline Meta-Reinforcement Learning

arXiv:2610.15428v1 Announce Type: cross Abstract: Context-based offline meta-reinforcement learning (COMRL) has advanced almost entirely through better task representations, which separate task ident...

By Chang-Hoon Jeong, Kisung Shin, Hanwool Sul, Byoung-Tak Zhang

reinforcement-learning benchmarks

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