arXiv:2511. 09190v2 Announce Type: replace Abstract: Hyperparameter Optimization (HPO) can lift the burden of tuning hyperparameters (HPs) of neural networks.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2606. 03073v1 Announce Type: cross Abstract: Reinforcement learning (RL) for large language models (LLMs) is highly sensitive to hyperparameter configurations, making hyperparameter optimization (HPO) essential yet computationally expensive.
arXiv:2607. 26680v1 Announce Type: new Abstract: Reinforcement learning (RL) has shown remarkable success across a wide range of complex tasks.
arXiv:2606. 02521v1 Announce Type: new Abstract: One-step text-to-image generators are attractive for deployment because they generate an image with a single forward pass, but preference finetuning them remains difficult: standard alignment methods often rely on policy likelihoods, denoising trajectories, differentiable reward gradients, or test-time optimization.
The paper introduces Repeated Optimizer Resampling (ROR), a method that treats optimizer choice as a hyperparameter and searches for the best optimizer during a single training run. ROR periodically scouts each candidate optimizer for a short number of epochs, then continues training with the best scout, allowing the optimizer to change over time. Experiments on MNIST, Fashion‑MNIST, and motor insurance claim‑count models show that one‑epoch ROR uses only 24–35% of the training effort required to exhaustively evaluate all optimizers while achieving comparable performance.
The paper introduces a reinforcement learning framework that selects among a portfolio of gradient‑based and derivative‑free optimizers during a run. At each decision point a recurrent policy reads the current run state and chooses both the next optimizer and its usage duration, passing the best solution and step size forward. The method is trained with a decoupled actor‑critic using the same runtime distribution metric as evaluation, and on unseen problems it outperforms all individual portfolio optimizers except at the smallest budgets, remaining robust to distribution shift.
One-step text-to-image generators are attractive for deployment because they generate an image with a single forward pass, but preference finetuning them remains difficult: standard alignment methods often rely on policy likelihoods, denoising trajectories, differentiable reward gradients, or test-time optimization. We propose Drifting Preference Optimization (DrPO), an online preference-finetuning method for deterministic one-step generators.