Large language models (LLMs) have shown great performance on complex reasoning tasks but often require generating long intermediate thoughts before reaching a final answer. During generation, LLMs rely on a key-value (KV) cache for autoregressive decoding. However, the memory footprint of the KV cache grows with output length. Prior work on KV cache optimization mostly focus on compressing the long input context, while retaining the full KV cache for decoding. For tasks requiring long output generation, this leads to increased computational and memory costs. In this paper, we introduce ZoomR, a novel approach that enables LLMs to adaptively compress verbose reasoning thoughts into summaries and uses a dynamic KV cache selection policy that leverages these summaries while also strategically "zooming in" on fine-grained details. By using summary keys as a coarse-grained index during decoding, ZoomR uses the query to retrieve details for only the most important thoughts. This hierarchical strategy significantly reduces memory usage by avoiding full-cache attention at each step. Experiments across math and reasoning tasks show that our approach achieves competitive performance compared to baselines, while reducing inference memory requirements by more than 4×. These results demonstrate that a multi-granularity KV selection enables more memory efficient decoding, especially for long output generation.
Figures & tables
Figure 1: Fine-grained details are often needed in order to generate the correct reasoning. Attending to only a compressed source of information like summaries can lead to incorrect intermediate steps.
Figure 2: An overview of the ZoomR workflow. Mean summary keys are computed and cached on GPU. When the KV cache selection needs to be updated, approximate attention scores are calculated across all layers. A global top- c selection identifies which summaries to zoom into. The final sparse KV cache loaded to GPU is a multi-granularity mix of an attention sink (green), selected detailed segments, summaries, and fixed recent window (blue). The symbolic attention map illustrates the dynamic nature of ZoomR during decoding.
Figure 3: Efficiency, consensus, and ablation studies. Fig. (a,b) shows the GPU memory and throughput during inference, respectively. Fig. (d,e) show consensus dynamics between correct and incorrect answers over time. Fig. (c,f) illustrates the relationships between c and k on performance and KV cache selection size.
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Parameter
Value
LoRA Rank ( r )
16
LoRA Alpha ( α )
32
Number of Epochs
3
Learning Rate
1×10−4
Batch Size
1
Gradient Accumulation Steps
4
Appendix
Table 2: LoRA Fine-tuning Hyperparameters
Figure 4: Prompt used to generate summaries
Figure 5: An example of a segment of reasoning process with intermediate summaries.
Figure 6: An example from AIME2025. ZoomR generated thoughts with summarization.
School of Computer Science and Technology, Beijing Jiaotong University · Microsoft · Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence