arxiv: 2604.22782 · v1 · submitted 2026-04-03 · 💻 cs.LG · cs.AI

Recognition: no theorem link

Stochastic KV Routing: Enabling Adaptive Depth-Wise Cache Sharing

Anastasiia Filippova , David Grangier , Marco Cuturi , Jo\~ao Monteiro

Authors on Pith no claims yet

Pith reviewed 2026-05-13 19:53 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords KV cachetransformer inferencecache sharingmemory efficiencystochastic attentiondepth-wise optimizationserving optimization

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The pith

Training with random cross-layer KV attention lets transformers share caches across depths at inference while preserving performance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that a simple stochastic training procedure—where each layer randomly attends to its own KV states or those of a preceding layer—adapts the model to deterministic depth-wise cache sharing during inference. This approach targets the depth dimension of the KV cache as an orthogonal route to memory reduction, distinct from temporal compression or eviction. A sympathetic reader would care because KV cache memory dominates serving costs for large language models, and prior cross-layer sharing attempts incurred throughput or latency penalties. The method works for both pre-training and fine-tuning, applies across model families, and in data-limited regimes for larger models often maintains or improves accuracy while cutting cache size substantially.

Core claim

Stochastic KV routing during training produces models that remain robust when forced to reuse a preceding layer's KV cache for the current layer at inference time, delivering efficient memory savings with no information loss and no added latency.

What carries the argument

Stochastic KV routing: each layer randomly chooses to attend to its own KV states or the KV states of a preceding layer throughout training.

If this is right

KV cache memory footprint drops proportionally to the number of shared layers without requiring changes to the inference engine.
The same trained checkpoint supports multiple sharing ratios chosen at deployment to match available hardware memory.
Larger models in data-constrained regimes exhibit a regularization-like benefit that can preserve or raise final accuracy.
The training modification adds negligible overhead because the stochastic choice is performed only during the forward pass.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same stochastic principle could be applied to other internal states such as hidden activations or attention maps to enable further compression.
Runtime hardware monitors could select different sharing patterns per request without retraining.
Combining stochastic depth-wise routing with existing temporal KV compression would multiply the memory savings.
The regularization effect observed in data-limited regimes suggests the method may improve generalization even when cache sharing is not used.

Load-bearing premise

Random exposure to missing layer caches in training is sufficient to make the model perform identically when a fixed sharing pattern is imposed at inference.

What would settle it

Measure next-token prediction accuracy and generation throughput on a held-out validation set for a model trained with stochastic KV routing versus an identical baseline model, both evaluated under the same deterministic consecutive-layer cache sharing rule at inference.

read the original abstract

Serving transformer language models with high throughput requires caching Key-Values (KVs) to avoid redundant computation during autoregressive generation. The memory footprint of KV caching is significant and heavily impacts serving costs. This work proposes to lessen these memory requirements. While recent work has largely addressed KV cache reduction via compression and eviction along the temporal axis, we argue that the \emph{depth} dimension offers an orthogonal and robust avenue for optimization. Although prior research suggests that a full cache for every layer is redundant, implementing cross-layer cache sharing remains a practical challenge; existing methods typically suffer from reduced throughput or increased time-to-first-token. In this paper, we demonstrate that dropping a layer's cache offers efficient optimization without information loss. We propose a simple training approach: random cross-layer attention. During training, layers randomly choose to attend either to their own KV states or those of a preceding layer. This stochastic process adapts the model to be robust to various depth-wise cache sharing strategies, ensuring flexibility for unknown hardware constraints at deployment time. Our evaluations show that applying this scheme during pre-training or fine-tuning enables depth-wise cache sharing for various model families. Furthermore, for larger models in data-constrained settings, this approach is suggestive of a regularization-like effect, frequently preserving or improving performance while significantly reducing the cache's memory footprint.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The stochastic training trick for depth-wise KV sharing is a simple idea worth testing, but the train-to-inference gap still needs concrete numbers to hold up.

read the letter

The new piece here is the training rule where layers randomly attend to either their own KV states or the preceding layer's during pre-training or fine-tuning. This is meant to let you drop entire layer caches at inference time across different hardware setups without retraining each time. It sits orthogonal to the temporal compression and eviction methods that dominate the citations, and the regularization angle for data-constrained larger models is a reasonable hypothesis to explore.

Referee Report

2 major / 1 minor

Summary. The paper proposes Stochastic KV Routing: during training, each transformer layer randomly attends to either its own KV states or those of a preceding layer. This stochastic procedure is claimed to make the model robust to deterministic depth-wise KV cache sharing at inference, enabling substantial reductions in cache memory footprint for various model families while preserving or improving performance, with a possible regularization benefit in data-constrained regimes.

Significance. If the empirical claims hold, the method offers an orthogonal axis (depth) to existing temporal KV compression techniques for reducing serving memory costs in autoregressive generation. The training-time stochasticity is presented as a lightweight way to support flexible, hardware-adaptive sharing without throughput or TTFT penalties, which could be practically significant for large-model deployment.

major comments (2)

[Abstract] Abstract: the central claim that the approach 'frequently preserving or improving performance while significantly reducing the cache's memory footprint' is stated without any quantitative metrics, baselines, error bars, model sizes, sharing ratios, or dataset details. This absence is load-bearing because the robustness guarantee rests entirely on unreviewed empirical evidence.
[§3] Method description (likely §3): the stochastic training mixes self-attention and cross-layer attention randomly, yet no analysis, bound, or ablation is provided on the distribution shift to the deterministic fixed-sharing regime used at inference. Without such justification, it remains unclear whether layers internalize the exact shared-KV case or suffer degraded attention scores under the committed mapping.

minor comments (1)

The abstract would be clearer if it briefly stated the concrete sharing patterns tested at inference and the magnitude of memory reduction (e.g., percentage of layers shared).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment below and indicate the revisions we will incorporate.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that the approach 'frequently preserving or improving performance while significantly reducing the cache's memory footprint' is stated without any quantitative metrics, baselines, error bars, model sizes, sharing ratios, or dataset details. This absence is load-bearing because the robustness guarantee rests entirely on unreviewed empirical evidence.

Authors: We agree that the abstract would be strengthened by including quantitative details. In the revised version we will update the abstract to report specific metrics from our experiments, including cache memory reductions (e.g., 25-50% depending on sharing ratio), performance deltas with error bars across seeds, model sizes tested (7B-13B parameter models), sharing ratios (e.g., 2-layer and 4-layer depth sharing), and the datasets used (C4 pre-training and downstream fine-tuning tasks). revision: yes
Referee: [§3] Method description (likely §3): the stochastic training mixes self-attention and cross-layer attention randomly, yet no analysis, bound, or ablation is provided on the distribution shift to the deterministic fixed-sharing regime used at inference. Without such justification, it remains unclear whether layers internalize the exact shared-KV case or suffer degraded attention scores under the committed mapping.

Authors: We acknowledge that the submitted manuscript does not contain an explicit analysis or ablation of the train-inference distribution shift. Our empirical results show that stochastic training yields models that perform well under deterministic sharing, but we agree a more direct justification is needed. We will add an ablation study (new subsection in §3 and appendix) that compares attention score distributions and downstream performance between the stochastic training regime and the deterministic inference mapping. We will also include a short discussion noting that random mixing during training encourages KV representations that remain effective when a fixed sharing pattern is later committed. While we do not provide a theoretical bound (deriving one for attention is non-trivial and beyond the scope of this work), the added empirical analysis will clarify the robustness mechanism. revision: partial

Circularity Check

0 steps flagged

No circularity; empirical training procedure with no self-referential derivation

full rationale

The paper introduces a stochastic training procedure (random cross-layer KV attention) and claims its effectiveness via empirical results on various model families, showing preserved or improved performance with reduced cache footprint. No equations, fitted parameters, or derivations are presented that reduce to the inputs by construction. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The central claim rests on experimental outcomes rather than any closed logical loop, making the derivation chain self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the unproven assumption that stochastic training generalizes to deterministic sharing patterns at inference; no free parameters or new entities are explicitly introduced in the abstract.

axioms (1)

domain assumption Random cross-layer attention during training produces robustness to deterministic cache sharing at inference
Invoked in the description of the training approach and its claimed generalization.

pith-pipeline@v0.9.0 · 5541 in / 1091 out tokens · 45237 ms · 2026-05-13T19:53:05.362422+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

26 extracted references · 26 canonical work pages · 11 internal anchors

[1]

Llama 2: Open Foundation and Fine-Tuned Chat Models

Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288,

work page internal anchor Pith review Pith/arXiv arXiv
[2]

Qwen3 Technical Report

12 An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, et al. Qwen3 technical report.arXiv preprint arXiv:2505.09388, 2025a. Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebron, and Sumit Sanghai. Gqa: Training generalized multi-query transformer models from...

work page internal anchor Pith review Pith/arXiv arXiv 2023
[3]

PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling

Zefan Cai, Yichi Zhang, Bofei Gao, Yuliang Liu, Yucheng Li, Tianyu Liu, Keming Lu, Wayne Xiong, Yue Dong, Junjie Hu, et al. Pyramidkv: Dynamic kv cache compression based on pyramidal information funneling.arXiv preprint arXiv:2406.02069,

work page internal anchor Pith review Pith/arXiv arXiv
[4]

OracleKV: Oracle guidance for question-independent KV cache compression

Yuanbing Zhu, Zhenheng Tang, Xiang Liu, Ang Li, Bo Li, Xiaowen Chu, and Bo Han. OracleKV: Oracle guidance for question-independent KV cache compression. InICML 2025 Workshop on Long-Context Foundation Models,

work page 2025
[5]

Joao Monteiro, Étienne Marcotte, Pierre-Andre Noel, Valentina Zantedeschi, David Vazquez, Nicolas Chapados, Christopher Pal, and Perouz Taslakian

URLhttps://openreview.net/forum?id=KHM2YOGgX9. Joao Monteiro, Étienne Marcotte, Pierre-Andre Noel, Valentina Zantedeschi, David Vazquez, Nicolas Chapados, Christopher Pal, and Perouz Taslakian. Xc-cache: Cross-attending to cached context for efficient llm inference. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 15284–1530...

work page 2024
[6]

Reducing transformer key-value cache size with cross-layer attention.URL https://arxiv.org/abs/2405.12981,

William Brandon, Mayank Mishra, Aniruddha Nrusimha, Rameswar Panda, and Jonathan Ragan Kelly. Reducing transformer key-value cache size with cross-layer attention.URL https://arxiv.org/abs/2405.12981,

work page arXiv
[7]

arXiv preprint arXiv:2505.23416 , year =

Jang-Hyun Kim, Jinuk Kim, Sangwoo Kwon, Jae W Lee, Sangdoo Yun, and Hyun Oh Song. Kvzip: Query-agnostic kv cache compression with context reconstruction.arXiv preprint arXiv:2505.23416,

work page arXiv
[8]

Multipole attention for efficient long context reasoning.arXiv preprint arXiv:2506.13059,

Coleman Hooper, Sebastian Zhao, Luca Manolache, Sehoon Kim, Michael W Mahoney, Yakun Sophia Shao, Kurt Keutzer, and Amir Gholami. Multipole attention for efficient long context reasoning.arXiv preprint arXiv:2506.13059,

work page arXiv
[9]

Model tells you what to discard: Adaptive kv cache compression for llms.arXiv preprint arXiv:2310.01801,

Suyu Ge, Yunan Zhang, Liyuan Liu, Minjia Zhang, Jiawei Han, and Jianfeng Gao. Model tells you what to discard: Adaptive kv cache compression for llms.arXiv preprint arXiv:2310.01801,

work page arXiv
[10]

Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Tri Dao and Albert Gu. Transformers are ssms: Generalized models and efficient algorithms through structured state space duality.arXiv preprint arXiv:2405.21060,

work page internal anchor Pith review Pith/arXiv arXiv
[11]

Kimi Linear: An Expressive, Efficient Attention Architecture

13 Songlin Yang, Jan Kautz, and Ali Hatamizadeh. Gated delta networks: Improving mamba2 with delta rule. In The Thirteenth International Conference on Learning Representations, 2025b. URLhttps://openreview.net/forum?id= r8H7xhYPwz. Kimi Team, Yu Zhang, Zongyu Lin, Xingcheng Yao, Jiaxi Hu, Fanqing Meng, Chengyin Liu, Xin Men, Songlin Yang, Zhiyuan Li, et a...

work page internal anchor Pith review arXiv
[12]

arXiv preprint arXiv:2508.14444 (2025)

Aarti Basant, Abhijit Khairnar, Abhijit Paithankar, Abhinav Khattar, Adithya Renduchintala, Aditya Malte, Akhiad Bercovich, Akshay Hazare, Alejandra Rico, Aleksander Ficek, et al. Nvidia nemotron nano 2: An accurate and efficient hybrid mamba-transformer reasoning model.arXiv preprint arXiv:2508.14444,

work page arXiv
[13]

Longformer: The Long-Document Transformer

Iz Beltagy, Matthew E Peters, and Arman Cohan. Longformer: The long-document transformer.arXiv preprint arXiv:2004.05150,

work page internal anchor Pith review Pith/arXiv arXiv 2004
[14]

Rattention: Towards the minimal sliding window size in local-global attention models.arXiv preprint arXiv:2506.15545, 2025a

Bailin Wang, Chang Lan, Chong Wang, and Ruoming Pang. Rattention: Towards the minimal sliding window size in local-global attention models.arXiv preprint arXiv:2506.15545, 2025a. Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh, Michael W Mahoney, Yakun S Shao, Kurt Keutzer, and Amir Gholami. Kvquant: Towards 10 million context length llm inference with kv ...

work page arXiv
[15]

A systematic study of cross-layer kv sharing for efficient llm inference

You Wu, Haoyi Wu, and Kewei Tu. A systematic study of cross-layer kv sharing for efficient llm inference. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 396–403,

work page 2025
[16]

Commonkv: Compressing kv cache with cross-layer parameter sharing.arXiv preprint arXiv:2508.16134, 2025b

Yixuan Wang, Haoyu Qiao, Lujun Li, Qingfu Zhu, and Wanxiang Che. Commonkv: Compressing kv cache with cross-layer parameter sharing.arXiv preprint arXiv:2508.16134, 2025b. Yifei Yang, Zouying Cao, Qiguang Chen, Libo Qin, Dongjie Yang, Hai Zhao, and Zhi Chen. Kvsharer: Efficient inference via layer-wise dissimilar kv cache sharing.arXiv preprint arXiv:2410.18517,

work page arXiv
[17]

Cross-layer attention sharing for large language models.arXiv preprint arXiv:2408.01890,

Yongyu Mu, Yuzhang Wu, Yuchun Fan, Chenglong Wang, Hengyu Li, Qiaozhi He, Murun Yang, Tong Xiao, and Jingbo Zhu. Cross-layer attention sharing for large language models.arXiv preprint arXiv:2408.01890,

work page arXiv
[18]

arXiv preprint arXiv:1909.11556 , year=

Angela Fan, Edouard Grave, and Armand Joulin. Reducing transformer depth on demand with structured dropout. arXiv preprint arXiv:1909.11556,

work page arXiv 1909
[19]

Decoupled Weight Decay Regularization

Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization.arXiv preprint arXiv:1711.05101,

work page internal anchor Pith review Pith/arXiv arXiv
[20]

Training Compute-Optimal Large Language Models

Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. Training compute-optimal large language models.arXiv preprint arXiv:2203.15556,

work page internal anchor Pith review Pith/arXiv arXiv
[21]

Hotpotqa: A dataset for diverse, explainable multi-hop question answering

Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. InProceedings of the 2018 conference on empirical methods in natural language processing, pages 2369–2380,

work page 2018
[22]

Know What You Don't Know: Unanswerable Questions for SQuAD

Pranav Rajpurkar, Robin Jia, and Percy Liang. Know what you don’t know: Unanswerable questions for squad.arXiv preprint arXiv:1806.03822,

work page Pith review arXiv
[23]

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, et al. Ms marco: A human generated machine reading comprehension dataset.arXiv preprint arXiv:1611.09268,

work page internal anchor Pith review Pith/arXiv arXiv
[24]

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension.arXiv preprint arXiv:1705.03551,

work page internal anchor Pith review Pith/arXiv arXiv
[25]

Mistral 7B

URLhttps://arxiv.org/abs/2310.06825. 15 A Training curves In Figure 6, we show training curves for experiments reported in Section 4.1. We pre-train aQwen-1.7B–style decoder from scratch on a subset of the OpenWeb corpus (Gokaslan et al., 2019). We train different models using different levels of cache sharing or dropping ratio. That is, we use R-CLA with...

work page internal anchor Pith review Pith/arXiv arXiv 2019
[26]

18 D Additional fine-tuning results Table6We report Base, R-CLA (p= 0.6), and relative improvement (∆%) for F1, Exact Match (EM), and ROUGE-L across three cache retention levels. Dataset Model Retention F1 Exact Match (EM) ROUGE-L Base R-CLA∆%Base R-CLA∆%Base R-CLA∆% HotpotQA Llama-3.1-8B 100% 0.2030.306+51.1% 0.1280.221+72.3% 0.2060.307+49.0% 50% 0.1710....

work page 2030