EntropyCache: Decoded Token Entropy Guided KV Caching for Diffusion Language Models

• URL: http://arxiv.org/abs/2603.18489v1
• Date: Thu, 19 Mar 2026 04:46:34 GMT
• Title: EntropyCache: Decoded Token Entropy Guided KV Caching for Diffusion Language Models
• Abstract summary: We propose EntropyCache, a training-free KV caching method that uses the maximum entropy of newly decoded token distributions as a constant-cost signal for deciding when to recompute.<n> Experiments on LLaDA-8B-Instruct and Dream-7B-Instruct show that EntropyCache achieves $15.2times$-$26.4times$ speedup on standard benchmarks and $22.4times$-$24.1times$ on chain-of-thought benchmarks, with competitive accuracy and decision overhead accounting for only $0.5%$ of inference time.
• Score: 8.323540970510809
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion-based large language models (dLLMs) rely on bidirectional attention, which prevents lossless KV caching and requires a full forward pass at every denoising step. Existing approximate KV caching methods reduce this cost by selectively updating cached states, but their decision overhead scales with context length or model depth. We propose EntropyCache, a training-free KV caching method that uses the maximum entropy of newly decoded token distributions as a constant-cost signal for deciding when to recompute. Our design is grounded in two empirical observations: (1) decoded token entropy correlates with KV cache drift, providing a cheap proxy for cache staleness, and (2) feature volatility of decoded tokens persists for multiple steps after unmasking, motivating recomputation of the $k$ most recently decoded tokens. The skip-or-recompute decision requires only $O(V)$ computation per step, independent of context length and model scale. Experiments on LLaDA-8B-Instruct and Dream-7B-Instruct show that EntropyCache achieves $15.2\times$-$26.4\times$ speedup on standard benchmarks and $22.4\times$-$24.1\times$ on chain-of-thought benchmarks, with competitive accuracy and decision overhead accounting for only $0.5\%$ of inference time. Code is available at https://github.com/mscheong01/EntropyCache.

Related papers

• Back from the Future: Key-Value Cache Management by Counter-Causal Surprise [67.1056509495879]
  Key-value (KV) cache management through compression and eviction strategies has emerged as an important research direction in recent years.<n>We propose a simple yet effective KV eviction scheme motivated by the insight that past tokens which can be well-predicted from more recent tokens are redundant.<n>We evaluate our strategy on various open-source LLMs and benchmark datasets showing competitive or improved performance over other state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-07-30T02:42:51Z)
• Nectar: Neural Estimation of Cached-Token Attention via Regression [36.709992864731866]
  We show how a compact neural network can be used to evaluate softmax attention over a fixed long context.<n>Nectar fits two networks per layer and KV-head: a target network that predicts the attention output and a score network that predicts the log-normalizer.<n>We report experiments on models from 1.7B to 8B parameters across five long-context datasets.
  arXiv  Detail & Related papers  (2026-05-10T21:51:36Z)
• SPA-Cache: Singular Proxies for Adaptive Caching in Diffusion Language Models [56.45983529954998]
  We present SPA-Cache that jointly optimize update identification and budget allocation in DLM cache.<n>First, we derive a low-dimensional singular proxy that enables the identification of update-critical tokens in a low-dimensional subspace.<n>Second, we introduce an adaptive strategy that allocates fewer updates to stable layers without degrading generation quality.
  arXiv  Detail & Related papers  (2026-01-30T05:22:44Z)
• Joint Encoding of KV-Cache Blocks for Scalable LLM Serving [3.3230675313521716]
  Existing KV-cache compression methods rely on rigids, disrupt tensor layouts, or require specialized compute.<n>We propose joint encoding of KV-cache blocks, which fuses similar blocks across requests and input chunks into shared representations.<n>This alleviates the KV-cache memory bottleneck, supporting high-concurrency serving without specialized hardware.
  arXiv  Detail & Related papers  (2026-01-06T14:50:58Z)
• KVReviver: Reversible KV Cache Compression with Sketch-Based Token Reconstruction [20.53279247581787]
  We propose KVReviver, a reversible KV cache compression method based on the sketch algorithm.<n>In 2k-length contexts, it requires only 10% of KV Cache budget while maintaining identical end-to-end inference accuracy.<n>For 32k-length contexts, it achieves equivalent or comparable accuracy 2% accuracy loss.
  arXiv  Detail & Related papers  (2025-12-01T03:59:20Z)
• Attention Is All You Need for KV Cache in Diffusion LLMs [36.94369617373333]
  Elastic-Cache performs adaptive, layer-aware cache updates for diffusion large language models.<n>Our method achieves significantly higher throughput ($6.8times$ on GSM8K) than existing confidence-based approaches.
  arXiv  Detail & Related papers  (2025-10-16T17:59:48Z)
• Catch Your Breath: Adaptive Computation for Self-Paced Sequence Production [55.76222360698305]
  We explore a class of supervised training objectives that allow a language model to dynamically and autonomously scale the number of compute steps used for each input token.<n>For any token, the model can request additional compute steps by emitting a don't know> output.<n>We find that the CYB model requests additional steps when doing so improves accuracy, and the model adapts its processing time to token-level complexity and context.
  arXiv  Detail & Related papers  (2025-10-13T21:07:05Z)
• d$^2$Cache: Accelerating Diffusion-Based LLMs via Dual Adaptive Caching [7.004421957218099]
  Diffusion-based large language models (dLLMs) suffer from inferior inference efficiency.<n>We introduce d$2$Cache, which is a training-free approximate KV cache framework for accelerating dLLM inference.
  arXiv  Detail & Related papers  (2025-09-27T04:07:23Z)
• dKV-Cache: The Cache for Diffusion Language Models [53.85291644298835]
  Diffusion Language Models (DLMs) have been seen as a promising competitor for autoregressive language models.<n>We propose a KV-cache-like mechanism, delayed KV-Cache, for the denoising process of DLMs.<n>Our approach is motivated by the observation that different tokens have distinct representation dynamics throughout the diffusion process.
  arXiv  Detail & Related papers  (2025-05-21T17:32:10Z)
• KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache [67.9776980972508]
  We develop a tuning-free 2bit KV cache quantization algorithm named KIVI. KIVI can enable Llama, Falcon, and Mistral models to maintain almost the same quality while using $mathbf2.6times$ less peak memory.
  arXiv  Detail & Related papers  (2024-02-05T06:06:47Z)
• DeepCache: Accelerating Diffusion Models for Free [65.02607075556742]
  DeepCache is a training-free paradigm that accelerates diffusion models from the perspective of model architecture. DeepCache capitalizes on the inherent temporal redundancy observed in the sequential denoising steps of diffusion models. Under the same throughput, DeepCache effectively achieves comparable or even marginally improved results with DDIM or PLMS.
  arXiv  Detail & Related papers  (2023-12-01T17:01:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.