arXiv AI
Jul 13

Self-Guided Test-Time Training for Long-Context LLMs

arXiv:2607. 09415v1 Announce Type: cross Abstract: Long-context processing has become increasingly important for large language models (LLMs), but simply extending the context window does not guarantee effective utilization of long inputs.

By Xinyu Zhu, Zhe Xu, Xiaohan Wei, Yunchen Pu, Fei Tian, Chonglin Sun, Kaushik Rangadurai, Hua Zhi, Frank Shyu, Sandeep Pandey, Luke Simon, Yu Meng, Xi Liu
arXiv Computation and Language
5d ago

Reinforced Fast Weights via Next-Sequence Prediction

The paper "Reinforced Fast Weights via Next-Sequence Prediction" introduces ReFINE, a method that extends the next-token prediction objective to next-sequence prediction for fast weight architectures. ReFINE selects informative token positions, generates multi-token rollouts, assigns sequence-level rewards, and optimizes the model using Group Relative Policy Optimization. Experiments on LaCT-760M and DeltaNet-1.3B demonstrate that ReFINE consistently outperforms standard next-token prediction fine‑tuning across retrieval, long‑context question answering, and LongBench tasks.

By Hee Seung Hwang, Xindi Wu, Sanghyuk Chun, Olga Russakovsky, Zhiwei Deng