Mamba State-Space Models Are Lyapunov-Stable Learners

• URL: http://arxiv.org/abs/2406.00209v2
• Date: Tue, 15 Oct 2024 19:21:58 GMT
• Title: Mamba State-Space Models Are Lyapunov-Stable Learners
• Authors: John T. Halloran, Manbir Gulati, Paul F. Roysdon,
• Abstract summary: Mamba state-space models (SSMs) were recently shown to outperform Transformer large language models (LLMs) across various tasks. We show that Mamba's recurrent dynamics are robust to small input changes. We also show that instruction tuning allows Mamba models to narrow this gap to 81% and Mamba-2 models to skyrocket over this gap to 132%.
• Score: 1.6385815610837167
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mamba state-space models (SSMs) were recently shown to outperform state-of-the-art (SOTA) Transformer large language models (LLMs) across various tasks. Despite subsequent widespread adaptation, little work has focused on Mamba LLMs' amenability for fine-tuning frameworks ubiquitously used for Transformer-based LLMs, e.g., mixed-precision fine-tuning (MPFT) and parameter-efficient fine-tuning (PEFT). For the former, it currently remains an open question whether Mamba's recurrent dynamics are robust to small input changes, such as those encountered during MPFT. Using dynamical systems theory (in particular, Lyapunov exponents), we answer this question in the affirmative. We empirically validate this result through several experiments, showing that Mamba SSMs are significantly more stable to changes introduced by mixed-precision than comparable Transformers, even when both MPFT and PEFT are combined. For PEFT, we show how targeting specific memory buffers in Mamba's customized CUDA kernels for low-rank adaptation regularizes SSM parameters, thus providing both parameter efficient learning and computational savings. Finally, with both MPFT and PEFT enabled, we explore the impact of instruction tuning Mamba SSMs for in-context learning (ICL) on natural language tasks. While pretrained Mamba and Mamba-2 models only achieve 38% and 82% (respectively) of the ICL improvements of comparable Transformer-based LLMs, we show that instruction tuning allows Mamba models to narrow this gap to 81% and Mamba-2 models to skyrocket over this gap to 132%.

Related papers

• Bifrost-1: Bridging Multimodal LLMs and Diffusion Models with Patch-level CLIP Latents [55.82787697101274]
  Bifrost-1 is a unified framework that bridges pretrained multimodal LLMs (MLLMs) and diffusion models.<n>By seamlessly integrating pretrained MLLMs and diffusion models with patch-level CLIP latents, our framework enables high-fidelity controllable image generation.<n>Our experiments demonstrate that Bifrost-1 achieves comparable or better performance than previous methods in terms of visual fidelity and multimodal understanding.
  arXiv  Detail & Related papers  (2025-08-08T02:38:47Z)
• Differential Mamba [16.613266337054267]
  Sequence models like Transformers and RNNs often overallocate attention to irrelevant context, leading to noisy intermediate representations.<n>Recent work has shown that differential design can mitigate this issue in Transformers, improving their effectiveness across various applications.<n>We show that a naive adaptation of differential design to Mamba is insufficient and requires careful architectural modifications.
  arXiv  Detail & Related papers  (2025-07-08T17:30:14Z)
• Memba: Membrane-driven Parameter-Efficient Fine-Tuning for Mamba [21.474315621757594]
  State Space Models (SSMs) have emerged as powerful alternatives to attention-based Transformers.<n>Memba is a membrane-driven.<n>PEFT approach specifically designed for Mamba.
  arXiv  Detail & Related papers  (2025-06-22T21:52:45Z)
• Routing Mamba: Scaling State Space Models with Mixture-of-Experts Projection [88.47928738482719]
  Linear State Space Models (SSMs) offer remarkable performance gains in sequence modeling.<n>Recent advances, such as Mamba, further enhance SSMs with input-dependent gating and hardware-aware implementations.<n>We introduce Routing Mamba (RoM), a novel approach that scales SSM parameters using sparse mixtures of linear projection experts.
  arXiv  Detail & Related papers  (2025-06-22T19:26:55Z)
• S'MoRE: Structural Mixture of Residual Experts for Parameter-Efficient LLM Fine-tuning [19.038272193170297]
  We propose Structural Mixture of Residual Experts (S'MoRE), a novel framework that seamlessly integrates the efficiency of LoRA with the flexibility of MoE.<n>By routing input tokens through sub-trees of residuals, S'MoRE emulates the capacity of numerous experts by instantiating and assembling just a few low-rank matrices.
  arXiv  Detail & Related papers  (2025-04-08T20:54:00Z)
• From Markov to Laplace: How Mamba In-Context Learns Markov Chains [36.22373318908893]
  We study in-context learning on Markov chains and uncover a surprising phenomenon.<n>Unlike transformers, even a single-layer Mamba efficiently learns the in-context Laplacian smoothing estimator.<n>These theoretical insights align strongly with empirical results and represent the first formal connection between Mamba and optimal statistical estimators.
  arXiv  Detail & Related papers  (2025-02-14T14:13:55Z)
• Mixture-of-Mamba: Enhancing Multi-Modal State-Space Models with Modality-Aware Sparsity [56.0251572416922]
  State Space Models (SSMs) have emerged as efficient alternatives to Transformers for sequential modeling. We propose a novel SSM architecture that introduces modality-aware sparsity through modality-specific parameterization of the Mamba block. We evaluate Mixture-of-Mamba across three multi-modal pretraining settings.
  arXiv  Detail & Related papers  (2025-01-27T18:35:05Z)
• Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement [54.427965535613886]
  Mamba, as a novel state-space model (SSM), has gained widespread application in natural language processing and computer vision. In this work, we introduce Mamba-SEUNet, an innovative architecture that integrates Mamba with U-Net for SE tasks.
  arXiv  Detail & Related papers  (2024-12-21T13:43:51Z)
• MobileMamba: Lightweight Multi-Receptive Visual Mamba Network [51.33486891724516]
  Previous research on lightweight models has primarily focused on CNNs and Transformer-based designs. We propose the MobileMamba framework, which balances efficiency and performance. MobileMamba achieves up to 83.6% on Top-1, surpassing existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-24T18:01:05Z)
• Bi-Mamba: Towards Accurate 1-Bit State Space Models [28.478762133816726]
  Bi-Mamba is a scalable and powerful 1-bit Mamba architecture designed for more efficient large language models. Bi-Mamba achieves performance comparable to its full-precision counterparts (e.g., FP16 or BF16) and much better accuracy than post-training-binarization (PTB) Mamba baselines.
  arXiv  Detail & Related papers  (2024-11-18T18:59:15Z)
• MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for Mamba [0.5530212768657544]
  Mamba, a State Space Model (SSM)-based model, has attracted attention as a potential alternative to Transformers. We investigate the effectiveness of existing PEFT methods for Transformers when applied to Mamba. We propose new Mamba-specific PEFT methods that leverage the distinctive structure of Mamba.
  arXiv  Detail & Related papers  (2024-11-06T11:57:55Z)
• Mamba for Scalable and Efficient Personalized Recommendations [0.135975510645475]
  We present a novel hybrid model that replaces Transformer layers with Mamba layers within the FT-Transformer architecture. We evaluate FT-Mamba in comparison to a traditional Transformer-based model within a Two-Tower architecture on three datasets.
  arXiv  Detail & Related papers  (2024-09-11T14:26:14Z)
• ReMamba: Equip Mamba with Effective Long-Sequence Modeling [50.530839868893786]
  We propose ReMamba, which enhances Mamba's ability to comprehend long contexts. ReMamba incorporates selective compression and adaptation techniques within a two-stage re-forward process.
  arXiv  Detail & Related papers  (2024-08-28T02:47:27Z)
• Integrating Multi-Modal Input Token Mixer Into Mamba-Based Decision Models: Decision MetaMamba [0.0]
  Sequence modeling with State Space models (SSMs) has demonstrated performance surpassing that of Transformers in various tasks. However, decision models based on Mamba, a state-of-the-art SSM, failed to achieve superior performance compared to enhanced Decision Transformers. We propose the Decision MetaMamba (DMM), which augments Mamba with a token mixer in its input layer.
  arXiv  Detail & Related papers  (2024-08-20T03:35:28Z)
• Extend Model Merging from Fine-Tuned to Pre-Trained Large Language Models via Weight Disentanglement [72.97553348776425]
  We make a pioneering effort to broaden the applicability of merging techniques from FT to PT LLMs. We introduce an approach based on WeIght DisENtanglement (WIDEN) to effectively extend the merging scope. We merge Qwen1.5-Chat (an FT LLM with instruction-following skills) with Sailor (a PT LLM with multilingual abilities) across 7B and 14B model scales.
  arXiv  Detail & Related papers  (2024-08-06T10:46:46Z)
• CoMMIT: Coordinated Multimodal Instruction Tuning [90.1532838391285]
  Multimodal large language models (MLLMs) generally involve cooperative learning between a backbone LLM and a feature encoder of non-text input modalities.<n>In this paper, we analyze the MLLM instruction tuning from both theoretical and empirical perspectives.<n>We propose a Multimodal Balance Coefficient that enables quantitative measurement of the balance of learning.
  arXiv  Detail & Related papers  (2024-07-29T23:18:55Z)
• Mamba-PTQ: Outlier Channels in Recurrent Large Language Models [49.1574468325115]
  We show that Mamba models exhibit the same pattern of outlier channels observed in attention-based LLMs. We show that the reason for the difficulty of quantizing SSMs is caused by activation outliers, similar to those observed in transformer-based LLMs.
  arXiv  Detail & Related papers  (2024-07-17T08:21:06Z)
• Mixture-of-Modules: Reinventing Transformers as Dynamic Assemblies of Modules [96.21649779507831]
  We propose a novel architecture dubbed mixture-of-modules (MoM) MoM is motivated by an intuition that any layer, regardless of its position, can be used to compute a token. We show that MoM provides not only a unified framework for Transformers but also a flexible and learnable approach for reducing redundancy.
  arXiv  Detail & Related papers  (2024-07-09T08:50:18Z)
• An Empirical Study of Mamba-based Language Models [69.74383762508805]
  Selective state-space models (SSMs) like Mamba overcome some shortcomings of Transformers. We present a direct comparison between 8B-context Mamba, Mamba-2, and Transformer models trained on the same datasets. We find that the 8B Mamba-2-Hybrid exceeds the 8B Transformer on all 12 standard tasks.
  arXiv  Detail & Related papers  (2024-06-12T05:25:15Z)
• Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks [25.092302463435523]
  State-space models (SSMs) have been proposed as alternatives to Transformer networks in language modeling. In this study, we evaluate the ICL performance of SSMs, focusing on Mamba, against Transformer models across various tasks.
  arXiv  Detail & Related papers  (2024-02-06T18:56:35Z)
• Is Mamba Capable of In-Context Learning? [63.682741783013306]
  State of the art foundation models such as GPT-4 perform surprisingly well at in-context learning (ICL) This work provides empirical evidence that Mamba, a newly proposed state space model, has similar ICL capabilities.
  arXiv  Detail & Related papers  (2024-02-05T16:39:12Z)
• MambaByte: Token-free Selective State Space Model [71.90159903595514]
  MambaByte is a token-free adaptation of the Mamba SSM trained autoregressively on byte sequences. We show MambaByte to be competitive with, and even to outperform, state-of-the-art subword Transformers on language modeling tasks.
  arXiv  Detail & Related papers  (2024-01-24T18:53:53Z)
• Simultaneous Machine Translation with Large Language Models [51.470478122113356]
  We investigate the possibility of applying Large Language Models to SimulMT tasks. We conducted experiments using the textttLlama2-7b-chat model on nine different languages from the MUST-C dataset. The results show that LLM outperforms dedicated MT models in terms of BLEU and LAAL metrics.
  arXiv  Detail & Related papers  (2023-09-13T04:06:47Z)

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.