The study investigates how gate initialisation and recurrent regularisers (dropout and zoneout) affect learning long dependencies in one‑layer LSTM and GRU networks on synthetic tasks. By running 720 factorial experiments that cross conventional and chrono gate initialisation with up to six regulariser placements, the authors find that chrono initialisation consistently enables networks to reach validation criteria within 5000 updates, whereas conventional initialisation often fails or learns slowly. Dropout generally delays learning, zoneout only helps in limited conditions, and early input sensitivity provides limited predictive power for successful training.
By Oleksandr Kholodniak, Oleksandr Prokhorov
The paper investigates how different optimizers acquire distinct generalizing relations in single‑hidden‑layer ReLU networks. By manipulating nuisance‑connected weights and performing a strict‑parity sweep, the authors show that training conditions reshape both shared failures and relative optimizer advantages, with specific interventions improving accuracy on hard stages. The study also compares optimizer behavior across tasks and an external architecture (CIFAR‑10).
By Gongyue Zhang, Honghai Liu
arXiv:2601. 18699v2 Announce Type: replace Abstract: Sequential fine-tuning of Large Language Models (LLMs) adaptation to target tasks often triggers catastrophic forgetting, where the acquisition of novel target skills degrades ancestral capabilities.
By Gustav Olaf Yunus Laitinen-Fredriksson Lundstrom-Imanov
arXiv:2610.09835v1 Announce Type: new
Abstract: Continual pre-training and fine-tuning in Large Language Models (LLMs) inevitably induce catastrophic forgetting, typically mitigated by replay using o...
By Jonghyun Han, Younghoon Song, Jongyoul Park
arXiv:2609.40063v1 Announce Type: cross
Abstract: Low-Rank Adaptive Residual Connections (LARC) give a frozen model a compact numerical state that can learn from feedback. The map $h+BAh$ adds a low-...
By Junyi Zou, Avrova Donz
The paper investigates when a language model actually learns task information versus when its readout (unembedding matrix) learns to express that information. Using a new parameter‑trajectory cross‑coding method, the authors find that hidden states can encode task data early in training, but the readout reorganizes only in a narrow window (0.4–1.1% of Pythia’s pretraining). By varying the readout learning rate, they show that faster readout learning reduces the gap between hidden‑state encoding and output expression without affecting final performance.
By Matteo He, William F. Shen, Alex Iacob, Andrej Jovanovic, Xinchi Qiu, Nicholas D. Lane
arXiv:2506. 21833v2 Announce Type: replace Abstract: Forward-mode automatic differentiation (FmAD) and zero-order (ZO) optimization are increasingly proposed as memory-efficient, backpropagation-free alternatives for large language model (LLM) fine-tuning, yet their benefits are typically evaluated only against standard backpropagation (BP), omitting memory-efficient variants such as activation checkpointing.
By Kunjal Panchal, Sunav Choudhary, Yuriy Brun, Hui Guan
The study investigates whether a neural network’s training history can better predict its future learning than its current state. Using small multilayer perceptrons trained under 42 different history regimes, the authors measured future learning at four checkpoints via a short probe that replicated the network for 100 updates on a new task. Results show that a low‑dimensional history state does not outperform a calibrated model of the current state for short‑term prediction, but in a companion screen of 1,560 synthetic regression runs, history models forecast final error better than current validation error after 12 epochs and remain comparable until 48 epochs.
By Martin Hofmann, Patrick M\"ader
The paper introduces LRE (Learned Relevance Eviction), a lightweight, CPU‑only, language‑model‑free scorer that learns which parts of an agent’s interaction history are task‑critical and preserves them verbatim. In experiments, LRE matches or surpasses baseline eviction policies on accuracy‑cost trade‑offs, recovers 93% of full‑history accuracy, reduces worst‑case prompt size by 52%, and outperforms dense and token‑pruning encoders in conversational memory while being 295–1569× smaller. The method also achieves superior budgeted answer quality on LoCoMo reading and can be trained annotation‑free, recovering 95% of supervised scorer performance.
By Nusrat Jahan Lia, Aritra Mazumder
arXiv:2609.37836v1 Announce Type: new
Abstract: Neural networks trained toward the same final objective can reach similar predictive performance while retaining internal representations shaped by ear...
By Ertu\u{g}rul Mutlu
arXiv:2607. 19390v1 Announce Type: new Abstract: A recent report finds that orthogonalizing the mLSTM memory matrix at read time (five Newton-Schulz iterations, trained through) substantially improves noisy associative recall.
By Keston Aquino-Michaels
arXiv:2607. 04333v1 Announce Type: new Abstract: Grokking -- generalization arriving long after training-set interpolation -- can be accelerated by structure-agnostic interventions: gradient filtering, weight-norm clamping, geometric penalties on hidden representations.
By Gunner Levi Howe