When to Memorize and When to Stop: Gated Recurrent Memory for Long-Context Reasoning
While reasoning over long context is crucial for various real-world applications, it remains challenging for large language models (LLMs) as they suffer from performance degradation as the context length grows. Recent work MemAgent has tried to tackle this by processing context chunk-by-chunk in an RNN-like loop and updating a textual memory for final answering. However, this naive recurrent memory update faces two crucial drawbacks: (i) memory can quickly explode because it can update indiscriminately, even on evidence-free chunks; and (ii) the loop lacks an exit mechanism, leading to unnecessary computation after even sufficient evidence is collected. To address these issues, we propose GRU-Mem, which incorporates two text-controlled gates for more stable and efficient long-context reasoning. Specifically, in GRU-Mem, the memory only updates when the update gate is open and the recurrent loop will exit immediately once the exit gate is open. To endow the model with such capabilities, we introduce two reward signals r^{update} and r^{exit} within end-to-end RL, rewarding the correct updating and exiting behaviors respectively. Experiments on various long-context reasoning tasks demonstrate the effectiveness and efficiency of GRU-Mem, which generally outperforms the vanilla MemAgent with up to 400\% times inference speed acceleration.
698d420e65c0d15a6d162108
Leheng Sheng
698d420e65c0d15a6d162109
Yongtao Zhang
698d420e65c0d15a6d16210a
Wenchang Ma
698d420e65c0d15a6d16210b
Yaorui Shi
claimed_verified
2026-02-12T13:28:35.189Z
63edd2d1f765928ceeb49057
https://cdn-avatars.huggingface.co/v1/production/uploads/1676530369930-noauth.png
Yaorui SHI
yrshi
user
yrshi
698d420e65c0d15a6d16210c
Ting Huang
698d420e65c0d15a6d16210d
Xiang Wang
698d420e65c0d15a6d16210e
An Zhang
698d420e65c0d15a6d16210f
Ke Shen
698d420e65c0d15a6d162110
Tat-Seng Chua
When to Memorize and When to Stop: Gated Recurrent Memory for Long-Context Reasoning
While reasoning over long context is crucial for various real-world applications, it remains challenging for large language models (LLMs) as they suffer from performance degradation as the context length grows. Recent work MemAgent has tried to tackle this by processing context chunk-by-chunk in an RNN-like loop and updating a textual memory for final answering. However, this naive recurrent memory update faces two crucial drawbacks: (i) memory can quickly explode because it can update indiscriminately, even on evidence-free chunks; and (ii) the loop lacks an exit mechanism, leading to unnecessary computation after even sufficient evidence is collected. To address these issues, we propose GRU-Mem, which incorporates two text-controlled gates for more stable and efficient long-context reasoning. Specifically, in GRU-Mem, the memory only updates when the update gate is open and the recurrent loop will exit immediately once the exit gate is open. To endow the model with such capabilities, we introduce two reward signals r^{update} and r^{exit} within end-to-end RL, rewarding the correct updating and exiting behaviors respectively. Experiments on various long-context reasoning tasks demonstrate the effectiveness and efficiency of GRU-Mem, which generally outperforms the vanilla MemAgent with up to 400\% times inference speed acceleration.
GRU-Mem addresses long-context reasoning challenges in LLMs by incorporating text-controlled gates and reinforcement learning rewards to stabilize memory updates and improve computational efficiency.
long-context reasoning large language models recurrent memory update text-controlled gates reward signals reinforcement learning memory explosion exit mechanism inference speed acceleration