arXiv:2604. 01577v3 Announce Type: replace-cross Abstract: We study out of distribution generalization in streaming tasks where models are trained on short sequences but must operate over much longer, unknown horizons under bounded memory.
By Shota Takashiro, Masanori Koyama, Takeru Miyato, Yusuke Iwasawa, Yutaka Matsuo, Kohei Hayashi
The paper examines whether a frozen recurrent network can supply sufficient memory for partially observable reinforcement learning tasks, instead of training the recurrent module jointly with the policy. Using a fixed recurrent network paired with a trainable output layer, the authors test this setup on various POMDPs with different memory demands, observation types, and action spaces. Results show that frozen networks can recall information over hundreds of steps and achieve competitive performance while using fewer than half the trainable parameters of fully trainable recurrent models.
The paper investigates whether a frozen recurrent network can provide sufficient memory for partially observable Markov decision processes (POMDPs). Using a fixed recurrent network combined with a trainable output module, the authors demonstrate that such networks can recall information over hundreds of steps and achieve competitive control performance across diverse POMDP tasks, while using fewer than half the trainable parameters of fully trainable recurrent networks. These findings suggest that learning the recurrent memory module is not always necessary, offering a simple, parameter‑efficient baseline for partially observable reinforcement learning.
By Zekang Wang, Zhe He, Gaojie Jin
arXiv:2107. 08183v2 Announce Type: replace Abstract: High-dimensional state and action spaces combined with sparse reward structures in reinforcement learning (RL) environments typically require advanced control architectures.
By JaeYoon Kim, Junyu Xuan, Christy Liang, Farookh Hussain
arXiv:2606. 10346v1 Announce Type: new Abstract: Reinforcement learning has become a key paradigm for eliciting reasoning abilities in large language models, where exploration is crucial for discovering effective solution trajectories.
By Jiangnan Xia, Yucheng Shi, Yu Yang, Kishan Panaganti, Zhenwen Liang, Ninghao Liu
arXiv:2608. 06595v1 Announce Type: cross Abstract: Neural networks applied to sequential decision-making tasks typically rely on latent representations of environment states.
By Mohamed Ghanem, Bernd Finkbeiner