Stepwise Token Selection for Efficient Multimodal Large Language Models

• URL: http://arxiv.org/abs/2606.16067v1
• Date: Sun, 14 Jun 2026 23:50:08 GMT
• Title: Stepwise Token Selection for Efficient Multimodal Large Language Models
• Abstract summary: We introduce a pointer-style selection mechanism that iteratively chooses informative tokens, conditioning each decision on previously selected ones, and dynamically determines when to stop.<n>Under aggressive pruning that removes 88.9% of visual tokens, our method preserves 94.6% of the original accuracy while achieving a 1.88x speed-up in prefill latency.
• Score: 0.7233065479782755
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In multimodal large language models (MLLMs), inference cost is largely dominated by the visual token prefix rather than the language backbone, making token reduction a key factor for improving efficiency. Existing approaches typically assign independent importance scores to visual tokens and retain a fixed number of top-ranked tokens, implicitly assuming token independence and a uniform compression ratio across inputs. In this work, we reformulate visual token pruning as a sequential decision-making process. Specifically, we introduce a pointer-style selection mechanism that iteratively chooses informative tokens, conditioning each decision on previously selected ones, and dynamically determines when to stop via a learned termination action. This enables joint optimization of both the selected subset and its size. To enable end-to-end training under standard language modeling objectives, we design a differentiable relaxation based on a variance-preserving noise interpolation scheme, allowing gradients to propagate through the discrete selection process. Extensive experiments on LLaVA-v1.5-7B and Qwen2.5-VL-7B demonstrate that our approach consistently outperforms fixed-ratio baselines across different compression levels. Under aggressive pruning that removes 88.9% of visual tokens, our method preserves 94.6% of the original accuracy while achieving a 1.88x speed-up in prefill latency.

Related papers

• Capturing Token Tendencies for Training-Free Token Pruning in Multimodal Large Language Models [86.02826269126308]
  We propose Trend-aware Pruning, a novel framework that elevates pruning from a local snapshot decision to a temporal trajectory modeling problem.<n>This enables a dynamic rectification mechanism that selectively reactivates "late-blooming" tokens, those initially undervalued but exhibiting rising semantic importance, thereby preventing the loss of critical visual cues.
  arXiv  Detail & Related papers  (2026-07-30T15:07:00Z)
• Attention-Discounted Adaptive Sampler for Masked Diffusion Language Models [59.51249894128724]
  Masked diffusion language models can reduce inference steps by revealing multiple tokens per denoising iteration.<n>We propose ADAS, a training-free reranking rule for parallel masked diffusion decoding.
  arXiv  Detail & Related papers  (2026-06-09T13:17:27Z)
• Beyond Surrogate Gradients: Fully Differentiable Token Pruning for Vision-Language Models [8.48481119028537]
  We propose DiffPrune, which reformulates pruning as continuous control of token information instead of discrete selection learning.<n>Across ten VLM benchmarks, DiffPrune retains 96.5% of full-model accuracy while accelerating LLM prefill by 2.85x, with only 0.69 ms of inference overhead.
  arXiv  Detail & Related papers  (2026-05-27T06:52:08Z)
• OmniSelect: Dynamic Modality-Aware Token Compression for Efficient Omni-modal Large Language Models [49.34557936004057]
  We propose a training-free, modality-adaptive token pruning framework that dynamically selects appropriate compression strategies for multimodal inputs.<n>Our method achieves efficient multimodal token reduction while maintaining strong performance, without requiring any additional training.
  arXiv  Detail & Related papers  (2026-05-18T08:33:52Z)
• ID-Selection: Importance-Diversity Based Visual Token Selection for Efficient LVLM Inference [58.28899599773308]
  ID-Selection is a simple yet effective token selection strategy for efficient LVLM inference.<n>It preserves informative tokens while reducing redundancy in a unified selection process.<n>Experiments across 5 LVLM backbones and 16 main benchmarks demonstrate that ID-Selection consistently achieves superior performance and efficiency.
  arXiv  Detail & Related papers  (2026-04-07T08:52:28Z)
• Beyond Attention Magnitude: Leveraging Inter-layer Rank Consistency for Efficient Vision-Language-Action Models [75.27089289058613]
  We introduce textbfTIES (textbfTau-guided textbfInter-layer textbfEfficient textbfSelection), a dynamic framework guided by inter-layer token ranking consistency.<n>On the CogACT + SIMPLER benchmark, TIES improves average success rates by 6% while reducing token usage by 78%, and demonstrate strong generalization across diverse decoders and benchmarks.
  arXiv  Detail & Related papers  (2026-03-26T02:13:03Z)
• D2Pruner: Debiased Importance and Structural Diversity for MLLM Token Pruning [49.16227597771663]
  D2Pruner is a framework that combines debiased importance with a structural pruning mechanism.<n>It reduces FLOPs by 74.2% while retaining 99.2% of its original performance.<n>It marks a significant advancement with up to 63. 53% improvement over existing methods.
  arXiv  Detail & Related papers  (2025-12-22T14:42:31Z)
• ssToken: Self-modulated and Semantic-aware Token Selection for LLM Fine-tuning [51.133569963553576]
  ssToken is a Self-modulated and Semantic-aware Token Selection approach.<n>We show that both self-modulated selection and semantic-aware selection alone outperform full-data fine-tuning.
  arXiv  Detail & Related papers  (2025-10-21T03:21:04Z)
• Training-Free Token Pruning via Zeroth-Order Gradient Estimation in Vision-Language Models [16.540220733551823]
  Large Vision-Language Models (VLMs) enable strong multimodal reasoning but incur heavy inference costs from redundant visual tokens.<n> Attention-based methods rely on raw attention scores, which are often unstable across layers and heads.<n>We propose ours, a training-free framework built on a simple intuition.
  arXiv  Detail & Related papers  (2025-09-29T14:20:05Z)
• Beyond Attention or Similarity: Maximizing Conditional Diversity for Token Pruning in MLLMs [30.97955016203357]
  In multimodal large language models, the length of input visual tokens is often significantly greater than that of their textual counterparts.<n>We propose a novel visual token pruning method named CDPruner, which maximizes the conditional diversity of retained tokens.<n>Our experiments show that CDPruner establishes new state-of-the-art on various vision-based benchmarks.
  arXiv  Detail & Related papers  (2025-06-12T17:59:09Z)

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.