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arxiv: 2406.07887 · v1 · submitted 2024-06-12 · 💻 cs.LG · cs.CL

An Empirical Study of Mamba-based Language Models

Roger Waleffe , Wonmin Byeon , Duncan Riach , Brandon Norick , Vijay Korthikanti , Tri Dao , Albert Gu , Ali Hatamizadeh
show 8 more authors
Sudhakar Singh Deepak Narayanan Garvit Kulshreshtha Vartika Singh Jared Casper Jan Kautz Mohammad Shoeybi Bryan Catanzaro
This is my paper

Pith reviewed 2026-05-18 10:25 UTC · model grok-4.3

classification 💻 cs.LG cs.CL
keywords Mambastate space modelshybrid architectureslanguage modelsTransformer comparisoninference efficiencylong contextempirical scaling
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The pith

The 8B Mamba-2-Hybrid outperforms a standard 8B Transformer on all twelve evaluated tasks while enabling much faster inference.

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

The paper trains 8B-parameter Mamba, Mamba-2, Transformer, and hybrid models on identical datasets of up to 3.5 trillion tokens to isolate architectural effects. Pure state-space models match or beat Transformers on many language tasks yet fall short on copying and in-context learning. The hybrid, which mixes Mamba-2 layers with limited attention and MLP layers, surpasses the Transformer baseline across every standard task while preserving the inference speed advantages of selective state-space models. Additional long-context tests up to 128K tokens show the hybrid continues to match or exceed Transformer performance on average.

Core claim

In a controlled comparison, the 8B Mamba-2-Hybrid architecture consisting of 43 percent Mamba-2, 7 percent attention, and 50 percent MLP layers exceeds the 8B Transformer by 2.65 points on average across twelve standard tasks and is projected to generate tokens up to eight times faster at inference time; the same hybrid remains competitive with the Transformer on twenty-three additional long-context tasks when both are extended to 16K, 32K, and 128K sequence lengths.

What carries the argument

The Mamba-2-Hybrid architecture, which interleaves selective state-space layers with a small number of attention layers to improve copying and in-context learning while retaining linear-time inference.

If this is right

  • Hybrid designs that combine a majority of Mamba-2 layers with a minority of attention layers can exceed pure Transformers on both short and long-context benchmarks.
  • Inference throughput gains of up to 8x become available without sacrificing task accuracy when the hybrid proportion is used.
  • Pure Mamba models remain limited on tasks that demand explicit copying or few-shot in-context learning even at 8B scale.
  • The released checkpoints allow direct reproduction and extension of the scaling behavior observed up to 3.5T tokens.
  • Long-context extensions of the hybrid maintain parity with Transformers when both architectures receive the same context-length adaptations.

Where Pith is reading between the lines

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

  • At still larger scales the inference-speed advantage of the hybrid could become decisive for production deployment where latency and memory costs dominate.
  • The optimal fraction of attention layers may vary by domain and could be tuned automatically rather than fixed at 7 percent.
  • The results suggest that selective state-space models benefit from targeted attention injection specifically for in-context reasoning rather than uniform replacement of all layers.

Load-bearing premise

The 8B models were trained under sufficiently identical data, optimizer, learning-rate schedule, and regularization conditions so that performance gaps can be attributed primarily to architecture.

What would settle it

Re-train an 8B Transformer using the exact same data mixture, optimizer, and schedule as the hybrid and measure whether the 2.65-point average gap disappears.

read the original abstract

Selective state-space models (SSMs) like Mamba overcome some of the shortcomings of Transformers, such as quadratic computational complexity with sequence length and large inference-time memory requirements from the key-value cache. Moreover, recent studies have shown that SSMs can match or exceed the language modeling capabilities of Transformers, making them an attractive alternative. In a controlled setting (e.g., same data), however, studies so far have only presented small scale experiments comparing SSMs to Transformers. To understand the strengths and weaknesses of these architectures at larger scales, we present a direct comparison between 8B-parameter Mamba, Mamba-2, and Transformer models trained on the same datasets of up to 3.5T tokens. We also compare these models to a hybrid architecture consisting of 43% Mamba-2, 7% attention, and 50% MLP layers (Mamba-2-Hybrid). Using a diverse set of tasks, we answer the question of whether Mamba models can match Transformers at larger training budgets. Our results show that while pure SSMs match or exceed Transformers on many tasks, they lag behind Transformers on tasks which require strong copying or in-context learning abilities (e.g., 5-shot MMLU, Phonebook) or long-context reasoning. In contrast, we find that the 8B Mamba-2-Hybrid exceeds the 8B Transformer on all 12 standard tasks we evaluated (+2.65 points on average) and is predicted to be up to 8x faster when generating tokens at inference time. To validate long-context capabilities, we provide additional experiments evaluating variants of the Mamba-2-Hybrid and Transformer extended to support 16K, 32K, and 128K sequences. On an additional 23 long-context tasks, the hybrid model continues to closely match or exceed the Transformer on average. To enable further study, we release the checkpoints as well as the code used to train our models as part of NVIDIA's Megatron-LM project.

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.

Referee Report

1 major / 2 minor

Summary. The paper presents a direct empirical comparison of 8B-parameter Mamba, Mamba-2, Transformer, and Mamba-2-Hybrid models trained on the same datasets of up to 3.5T tokens. Pure SSMs match or exceed Transformers on many tasks but lag on copying/in-context learning and long-context reasoning; the Mamba-2-Hybrid (43% Mamba-2, 7% attention, 50% MLP) exceeds the Transformer on all 12 standard tasks by +2.65 points on average, maintains parity on 23 additional long-context tasks up to 128K, and is predicted to offer up to 8x faster inference. Checkpoints and training code are released.

Significance. If training conditions are equivalent, the work supplies concrete evidence that hybrid SSM-attention models can outperform pure Transformers at the 8B scale while delivering inference efficiency gains. The public release of checkpoints and Megatron-LM code is a clear strength for reproducibility and follow-on research.

major comments (1)
  1. The central claim that the +2.65 average gain is attributable to the hybrid layer mix requires that data, optimizer, learning-rate schedule, and regularization were identical across the 8B Transformer and Mamba-2-Hybrid. The manuscript states only that models were 'trained on the same datasets of up to 3.5T tokens' and supplies no table or section listing per-model values for peak LR, decay, Adam betas, weight decay, or clipping. Even modest differences in these settings could produce score shifts comparable to the reported margin.
minor comments (2)
  1. Reporting standard deviation across random seeds for the 8B models would strengthen defensibility of the architectural conclusion, especially for the headline +2.65 average.
  2. The long-context section should explicitly state whether the 16K/32K/128K variants were trained from scratch or obtained via continued pre-training / fine-tuning of the base models.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and recommendation for minor revision. We address the concern regarding training configuration details below.

read point-by-point responses

  1. Referee: The central claim that the +2.65 average gain is attributable to the hybrid layer mix requires that data, optimizer, learning-rate schedule, and regularization were identical across the 8B Transformer and Mamba-2-Hybrid. The manuscript states only that models were 'trained on the same datasets of up to 3.5T tokens' and supplies no table or section listing per-model values for peak LR, decay, Adam betas, weight decay, or clipping. Even modest differences in these settings could produce score shifts comparable to the reported margin.

    Authors: We agree that explicit documentation of the full training configuration is necessary to substantiate that performance differences arise from the layer mix rather than hyperparameter variations. All models were trained under identical conditions in the Megatron-LM framework, using the same datasets, optimizer settings, peak learning rate, decay schedule, Adam betas, weight decay, and gradient clipping. To improve transparency, we will add a new table in the revised manuscript that lists these per-model hyperparameter values. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical comparisons with no derivations or fitted predictions

full rationale

The manuscript is an empirical study that trains 8B-parameter Mamba, Mamba-2, Transformer, and Mamba-2-Hybrid models on identical datasets of up to 3.5T tokens and reports direct performance measurements on 12 standard tasks plus 23 long-context tasks. No equations, fitted parameters, or mathematical derivations appear; the central claim that the hybrid exceeds the Transformer by +2.65 points on average is presented as an observed outcome of independently trained models rather than a prediction derived from any model equation or self-citation. No self-citations are invoked to establish uniqueness theorems, ansatzes, or load-bearing premises. The analysis is therefore self-contained against external benchmarks of model performance.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central performance claims rest on the validity of the controlled training protocol and the assumption that the chosen 12+23 tasks are representative of real-world capabilities.

axioms (1)
  • domain assumption Models trained on identical data and comparable optimization settings allow direct attribution of performance differences to architecture
    Explicitly invoked in the abstract as 'controlled setting (e.g., same data)'

pith-pipeline@v0.9.0 · 5967 in / 1406 out tokens · 44242 ms · 2026-05-18T10:25:57.149983+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

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    models were 'trained on the same datasets of up to 3.5T tokens' but supplies no table or section listing per-model values for peak LR, decay, Adam betas, weight decay, or clipping

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Forward citations

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