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MLAN: Language-Based Instruction Tuning Preserves and Transfers Knowledge in Multimodal Language Models

T0 review · 3 major / 7 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper claims that instruction tuning of multimodal language models with a text-heavy mixture (75% text-only, 25% vision-language) under a fixed 186,000-instance budget matches or outperforms vision-heavy mixtures on both text and…

desk verdict Useful controlled sweep on modality ratios with token accounting; the vision-parity claim needs seeds and a validation split before it's believed. read the letter →

arxiv 2411.10557 v3 pith:TMM7B6CX submitted 2024-11-15 cs.CL

Jianhong Tu , Zhuohao Ni , Nicholas Crispino , Zihao Yu , Michael Bendersky , Beliz Gunel , Ruoxi Jia , Xin Liu
show 3 more authors
Lingjuan Lyu Dawn Song Chenguang Wang
This is my paper · ORCID
classification cs.CL
keywords instructiontuningmultimodallargelanguagemodelszero-shotgeneralizationtext-onlydatavision-languagetransfertrainingefficiencymixturecatastrophicforgetting
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper claims that, after multimodal pretraining, the instruction tuning stage of a multimodal language model does not need to be dominated by image-text data. It proposes MLAN, a fixed-budget mixture that is 75% text-only and 25% vision-language instruction data, trained on 186,000 instances sampled from Super-NaturalInstructions and Vision-Flan. Across 12 held-out text and vision benchmarks, this text-heavy mixture matches or slightly beats the vision-heavy mixtures used by LLaVA-1.5 and Cambrian-1 on two Llama-based models, while seeing fewer than half the images and processing up to about half the training tokens. The paper's explanation is that instruction-following ability and domain knowledge are mostly language-borne once vision-language alignment is done, so a small amount of vision data suffices for grounding. If correct, this would make multimodal instruction tuning substantially cheaper and more reliant on diverse text-only task supervision.

What carries the argument

The load-bearing mechanism is task-level semantic alignment between text-only and vision-language instruction data. The paper samples 100,000 instruction prompts from Super-NaturalInstructions and Vision-Flan, embeds them with a pretrained sentence transformer, and reports a significantly non-negative mean cosine similarity between the two modalities, reasoning that tasks are defined by their instructions and that shared task semantics transfer once vision-language pretraining aligns image tokens with text tokens. The method itself is a controlled training recipe: a fixed 186,000-instance budget, FLAN-style formatting, a CLIP-ViT-L/14@336 visual encoder with a two-layer MLP projector, and a fully unfrozen LLM, with only the data composition varied across experiments.

What would settle it

Evaluate MLAN on vision tasks with no close text-only analogue, such as OCR, fine-grained object grounding, or object counting in images, under the same 186,000-instance budget; if the 75% text-heavy mixture falls clearly below the vision-heavy Cambrian-1 mixture on those benchmarks, the claim that language-based tuning generally preserves and transfers vision knowledge is falsified.

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Extended reading notes

Core claim

The central discovery is that instruction tuning of a multimodal LLM can be re-oriented around text without losing vision performance. Under a fixed budget of 186,000 training instances, the MLAN mixture of 75% text-only and 25% vision-language instructions yields held-out zero-shot performance on both modalities that is on par with or better than vision-heavy recipes (MIX-LLaVA-1.5 with 6% text and MIX-Cambrian-1 with 25% text). On the language benchmarks, MLAN averages 64.50 versus 63.18 for MIX-Cambrian-1 on Llama-3.2-3B and 71.57 versus 67.76 on Llama-3.1-8B; on vision benchmarks it averages 59.13 versus 58.57 on the 3B model and trails MIX-Cambrian-1 by 0.33 points on the 8B model. The text-heavy model processes 60.1 million training tokens, compared with 101.5 million for the Cambrian-1 mix and 117.2 million for the LLaVA-1.5 mix, a reduction of roughly 40% to 49%. The paper argues the transfer is possible because text-only and vision-language instructions share task-level semantics, and its controlled ablation shows that even 12.5% text-only data sharply raises both text and vision scores.

Load-bearing premise

The transfer mechanism assumes that cosine similarity between embedded text-only and vision-language instruction prompts is a reliable proxy for whether abilities learned from text-only data will transfer to image-grounded tasks; if that similarity is not the right proxy, the motivation for the text-heavy mixture weakens even if the fixed-budget empirical results still hold.

Editorial extensions

If this is right

  • A fixed-budget instruction tuning set can be made 75% text-only without sacrificing held-out vision performance, on both a 3B and an 8B Llama-based multimodal model.
  • Vision-heavy instruction mixtures (6% to 25% text) erode language knowledge on datasets like CommonsenseQA and CosmosQA by up to 20 percentage points, while the text-heavy mixture largely avoids that degradation.
  • Because CLIP converts each image into 576 visual tokens, replacing vision instances with text instances at the same instance budget cuts the number of training tokens processed by roughly half.
  • The mixture ratio is not arbitrary: 12.5% text-only data already produces a sharp gain on both text and vision axes, and vision performance peaks at a moderate language share, showing that neither pure modality is sufficient.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If text-heavy tuning generalizes beyond the two models tested here, the cost bottleneck of multimodal instruction tuning shifts from collecting image-text pairs to assembling diverse text-only task mixtures; a testable extension is to select text-only tasks whose instruction embeddings are most similar to a target vision benchmark and measure the resulting vision score.
  • The cosine-similarity analysis implies a practical data-selection tool: embed candidate text-only and vision-language instructions, then choose cross-modally similar subsets instead of fixing ratios by hand.
  • The asymmetric forgetting pattern — text abilities erode under vision-heavy tuning while vision abilities improve under text tuning — suggests language knowledge is the fragile resource in multimodal models, a prediction that could be checked on other architectures and other pretraining corpora.
  • The paper's own limitation section notes that OCR, captioning, and other specialized out-of-distribution vision tasks were not evaluated; a natural next test is whether the 75/25 mixture holds up when those tasks are added to the benchmark suite.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. The paper proposes MLAN, a visual instruction tuning strategy that replaces most vision-language instruction data with text-only data (75% text, 25% vision) under a fixed 186k-instance budget after multimodal pretraining. Using LLaVA-style models built on Llama-3.2-3B and Llama-3.1-8B, the authors compare MLAN against simplified mixtures with LLaVA-1.5-style (6% text) and Cambrian-1-style (25% text) ratios on seven text-only and five vision-language held-out benchmarks. The headline finding is that MLAN matches or exceeds these vision-heavy proxy mixtures on vision benchmarks, clearly improves text benchmarks, and processes roughly 40-50% fewer training tokens. Additional experiments explore the language-data ratio curve, task diversity, pretraining data, and instruction-tuned versus base backbones.

Significance. If the result is robust to seed variation and does not come from selection on the evaluation benchmarks, this is a valuable and cost-saving empirical contribution. The paper addresses an underexplored design choice (modality composition in MLLM instruction tuning) with controlled fixed-budget comparisons at two model scales, includes token-level efficiency accounting, and provides several ablations. It is also candid about limitations, including the narrow architecture scope and the absence of specialized tasks such as OCR and captioning. However, the central 'matches or exceeds' claim currently rests on small vision-side margins from single runs, and the chosen 75% ratio appears to have been selected from the same evaluation curves used in the main tables. These issues need to be addressed before the quantitative conclusion can be considered reliable.

major comments (3)
  1. [§3.1, Tables 1–2] All results in the main comparison are single runs, with no error bars, standard deviations, or repeated-seed analysis reported anywhere in the manuscript. The vision-side difference that supports 'matches or exceeds' is extremely small for the larger model: MLAN's vision average is 62.25 versus 62.58 for MIX-Cambrian-1 in Table 1, a 0.33-point deficit, while individual benchmarks trade by several points (MMMU 34.44 vs 36.00; MMBench 72.51 vs 73.50; POPE 81.84 vs 82.57). Under sampling or optimization noise, that deficit could easily become a multi-point gap, which would change the conclusion from 'matches or exceeds' to 'slightly worse on vision with large text gains.' Please provide at least three seeds with error bars or confidence intervals, or explicitly restrict the claim to 'comparable on vision' with the uncertainty stated. The Limitations section does not currently address this single-run variance.
  2. [§3.3, Figure 3] The paper selects the 75% text-only ratio for MLAN after inspecting the knowledge-transfer curves in Figure 3, which are computed on the same held-out evaluation benchmarks used in Tables 1 and 2; no separate validation split is described. If the ratio was chosen by peeking at these curves, the reported comparison is optimistically selected rather than a prediction, and the 0.33-point vision deficit could reflect overfitting to the chosen evaluation suite. Please either document that the ratio was fixed before evaluation, or introduce a validation split (for example, hold out a subset of the 12 benchmarks for ratio selection and report the remaining benchmarks as the headline results).
  3. [§3.1, Table 3 and Appendix D.1] The baselines called 'MIX-LLaVA-1.5' and 'MIX-Cambrian-1' are not the actual LLaVA-1.5 or Cambrian-1 instruction tuning recipes: they are simplified mixtures sampled from the same two datasets (Super-NaturalInstructions and Vision-Flan) with 6% and 25% text-only ratios at a fixed 186k-instance budget. Actual LLaVA-1.5 uses 665k instances and Cambrian-1 uses millions of instances with different data sources, as Table 8 itself shows. Therefore, the conclusion that MLAN 'matches or better performance' on downstream vision-language tasks compared with these state-of-the-art recipes overstates what is directly supported. Please either compare against the real recipes (ideally at matched token budgets) or rename the baselines to 'proxy mixtures with LLaVA-1.5/Cambrian-1 text ratios' and avoid the state-of-the-art claim.
minor comments (7)
  1. [Table 1, Appendix A, Table 7] The treatment of ScienceQA is inconsistent: the Table 1 note says it is 'included in Vision-Flan but excluded in experiments,' whereas Appendix A says it is removed from the training set for evaluation, and Table 7 lists it as an evaluation benchmark. Please clarify whether it was excluded from training or from evaluation, or both, and correct the wording accordingly.
  2. [§2.1, Figure 4] The statistical tests for cosine similarity report only p-values; please also report effect sizes and confidence intervals, since with 100k samples a 'significantly non-negative mean' is a weak statement that does not quantify how similar the instructions actually are.
  3. [§3.3, Figure 3] The knowledge-transfer curve would be more informative with per-benchmark values and error bars; currently the text 'peaks and then slightly declines' cannot be quantitatively verified from the figure.
  4. [§3.4, Table 4] Because most production MLLMs use an instruction-tuned chat backbone, the 'Instruct LLM' row is an important caveat: the main comparison in Tables 1–2 uses non-instruction base models, so the practical significance of MLAN for standard recipes remains unclear. Please discuss whether the main conclusions hold with chat backbones.
  5. [Appendix D.1, Table 8] The Cambrian-1 row in Table 8 is garbled ('Cambrian-1 (Tong et al., 2024) – Cambrian-7M 1.68M ∼7M 23.8%'); please fix the formatting so the dataset size and text-only percentage are unambiguous.
  6. [§2.3, Tables 6–7] The text states '12 comprehensive benchmarks' while Tables 6–7 list 13 datasets because ARC-E and ARC-C are reported separately; please align the count.
  7. [Appendix B / Data Availability] No code, configuration, or checkpoint release is mentioned; given that the headline depends on exact data sampling and the 75% ratio, releasing the data mixture and training configuration would substantially improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: MLAN is an empirical data-mixture study judged against external held-out benchmarks, and no derivation reduces to its own inputs.

full rationale

The paper's central claim is empirical: under a fixed 186,000-instance budget, a 75% text-only / 25% vision-language instruction tuning mixture matches or exceeds vision-heavy mixtures on held-out text and vision benchmarks. This claim is tested against external datasets (ARC, MMLU, POPE, MMMU, MME, MMBench, etc.) and against two baseline mixtures, MIX-LLaVA-1.5 and MIX-Cambrian-1, which are reproduced under the same training protocol. MLAN is not derived from the evaluation scores; the 75% ratio is a dataset-composition choice reported as a method, and the main results are direct benchmark measurements. The cosine-similarity analysis in Section 2.1 is motivational rather than derivational: it supports the hypothesis that instruction semantics are shared across modalities, but the transfer claim is not computed from those similarity scores. There is no equation in which the output is identical to an input by construction, and no parameter is fitted to a subset of data and then renamed as a prediction. The paper does not rely on a load-bearing self-citation or an imported uniqueness theorem; its architecture and training recipe follow standard LLaVA practice with external references. The most legitimate concern is that the 75% ratio appears to have been selected after inspecting the knowledge-transfer curve in Figure 3 on the same evaluation benchmarks used in the main tables, with no validation split or repeated seeds described. That is a selection-bias and reproducibility concern about the strength of the 'matches or exceeds' claim, not a definitional or constructional circularity, because the reported numbers are not forced to equal the curve by construction. Accordingly, the appropriate finding is no significant circularity.

Assumptions & free parameters 2 free parameters · 2 assumptions · 0 invented entities

MLAN is a data mixture recipe rather than a new physical or architectural entity. The central result rests on public datasets and standard LLaVA-style components; the main assumed inputs are the transfer hypothesis and the fixed training budget.

free parameters (2)
  • Text-only ratio for MLAN = 75% text-only, 25% vision-language
    The headline mixture was selected after observing the ratio sweep in Figure 3 on evaluation benchmarks, not on a separate validation set.
  • Instruction tuning instance budget = 186,000
    A fixed budget chosen by the authors to make comparisons fair; the absolute size is arbitrary and may not transfer to larger training regimes.
assumptions (2)
  • domain assumption Strong visual pretraining alignment makes image tokens functionally similar to text tokens for instruction following
    Stated in Sections 2.1 and 2.2 as the reason text-only instruction tuning can transfer to vision; not directly measured except through downstream results.
  • domain assumption Cosine similarity of instruction embeddings (all-mpnet-base-v2) is a valid measure of cross-modal task transferability
    Used in Section 2.1 and Figure 2 to motivate MLAN; similarity of instruction text is treated as evidence about transfer of learned abilities.

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Pith. "Pith review of MLAN: Language-Based Instruction Tuning Preserves and Transfers Knowledge in Multimodal Language Models." pith.science (2026). https://pith.science/paper/TMM7B6CX

@misc{pith2026241110557,
  author       = {Pith},
  title        = {Pith review of: MLAN: Language-Based Instruction Tuning Preserves and Transfers Knowledge in Multimodal Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TMM7B6CX}},
  note         = {Machine review of arXiv:2411.10557}
}
read the original abstract

We present a novel visual instruction tuning strategy to improve the zero-shot task generalization of multimodal large language models by building a firm text-only knowledge base. Existing work lacks sufficient experimentation on the importance of each modality in the instruction tuning stage, often using a majority of vision-language data while keeping text-only data limited and fixing mixtures of modalities. By incorporating diverse text-only data in the visual instruction tuning stage, we vary vision-language data in various controlled experiments to investigate the importance of modality in visual instruction tuning. Our comprehensive evaluation shows that the text-heavy instruction tuning approach is able to perform on-par with traditional vision-heavy mixtures on both modalities across 12 general datasets while using as low as half the total training tokens. We find that simply increasing sufficiently diverse text-only data enables transfer of instruction following ability and domain knowledge across modalities while being more efficient than the vision-language approach.

Figures

Figures reproduced from arXiv: 2411.10557 by the authors.

Figure 1
Figure 1. Overview of MLAN. (a) MLAN represents a shift in perspective towards text during instruction tuning. After vision-language pretraining, we include diverse text-only data in our instruction tuning mixture spanning many tasks. We emphasize including text-only data to show the transferability of instruction tuning across modalities. For evaluation, we select ample text-only and vision-language datasets, allowing us to … view at source ↗
Figure 2
Figure 2. Similarity between text-only and vision-language instruction tuning data shown both (a) quantita￾tively with similarity scores and (b) qualitatively with examples. 100k instructions are sampled from the Super-NaturalInstructions (Wang et al., 2022b) and Vision-Flan (Xu et al., 2024) datasets and embedded by a pretrained sentenceTransformer, all-mpnet-base-v2 (Song et al., 2020). The red vertical line denotes the mea… view at source ↗
Figure 3
Figure 3. Average scores on Llama-3.2-3B based MLLMs with respect to the percentage of language data mixed in. The percentage denotes the amount of language data. Base LLM Variant Text Avg. Vision Avg. Llama-3.2-3B +MLAN 64.50 59.13 +Instruct LLM 67.74 60.98 -25% tasks 65.68 55.71 -50% tasks 65.98 55.73 -75% tasks 66.35 56.79 [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Distribution of the cosine similarity of [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

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    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.