For-Value: Efficient Forward-Only Data Valuation for finetuning LLMs and VLMs
Pith reviewed 2026-05-18 22:21 UTC · model grok-4.3
The pith
Data valuation for LLMs and VLMs reduces to alignment between final hidden representations and last-layer prediction errors, computable in one forward pass.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Leveraging the expressive power of pretrained LLMs and VLMs, the authors show that data valuation is captured by the alignment between final hidden representations and prediction errors at the last layer. In light of this result For-Value evaluates each data point with a simple closed-form expression after a single forward pass, removing any need for backpropagation.
What carries the argument
The alignment between final hidden representations and last-layer prediction errors, used as a direct proxy that substitutes for gradient-based influence scores.
If this is right
- For-Value matches or outperforms gradient-based baselines on influential-data detection.
- For-Value matches or outperforms gradient-based baselines on mislabeled-data detection.
- The method supports efficient batch-parallel computation because it requires only forward passes.
- Significant runtime and memory savings are realized by eliminating backpropagation entirely.
Where Pith is reading between the lines
- The same alignment proxy might apply to other pretrained architectures once the final hidden state and output error are defined analogously.
- The closed-form expression could be inserted into online data-selection loops during continued pretraining without extra gradient overhead.
- If the alignment holds, practitioners could audit very large web-scale datasets by processing them in a single forward sweep rather than repeated influence-function runs.
Load-bearing premise
Data valuation for finetuning reduces exactly to the alignment between final hidden representations and last-layer prediction errors.
What would settle it
Remove or reweight the highest-scoring points according to For-Value, retrain or finetune the model, and check whether the resulting change in validation performance differs from the change obtained by random or gradient-based selection.
read the original abstract
Data valuation is essential for enhancing the transparency and accountability of large language models (LLMs) and vision-language models (VLMs). However, existing methods typically rely on gradient computations, making them computationally prohibitive for billion-parameter models and precluding batch parallelization. In this work, we introduce For-Value, a forward-only data valuation framework that enables efficient batch-scalable value estimation while maintaining effectiveness. Leveraging the expressive power of pretrained LLMs/VLMs, we theoretically demonstrate that data valuation can be captured by the alignment between the final hidden representations and prediction errors at the last layer. In light of this insight, For-Value computes data value using a simple closed-form expression with a single forward pass, eliminating the need for costly backpropagation and enabling efficient batch calculating at scale. Extensive experiments show that For-Value matches or outperforms gradient-based baselines in detecting influential data and mislabeled data, while achieving significant efficiency improvements.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript introduces For-Value, a forward-only data valuation method for finetuning LLMs and VLMs. It theoretically demonstrates that data valuation reduces to the alignment between final hidden representations and last-layer prediction errors, yielding a closed-form expression computable with a single forward pass. Experiments indicate that For-Value matches or outperforms gradient-based baselines on influential-data and mislabeled-data detection tasks while providing substantial efficiency gains through batch-parallelizable forward-only computation.
Significance. If the claimed exact reduction holds under standard LLM/VLM finetuning losses and architectures, the work would represent a meaningful advance by removing the backpropagation barrier that currently limits data valuation to small models or small batches. The closed-form, single-pass design and explicit leveraging of pretrained representations are strengths that could enable practical scaling.
major comments (2)
- [§3.1] §3.1, Eq. (3)–(7): The derivation that data valuation equals the inner product between final hidden states and prediction errors is presented as exact, yet the steps invoke a linear last-layer assumption and appear to start from a quadratic loss; no explicit error bound or extension to cross-entropy loss (standard for LLM/VLM heads) is supplied. Because this equivalence is the sole justification for replacing influence functions with the forward-only formula, the missing assumptions and error analysis are load-bearing.
- [§4.2] §4.2, Table 2: The reported superiority over TracIn and DataInf on mislabeled-data detection is shown only for a single random seed per dataset; without variance estimates or statistical tests, it is impossible to determine whether the observed gains are robust or merely consistent with the heuristic nature of the alignment quantity.
minor comments (2)
- [§3.1] Notation for the alignment quantity (denoted A in Eq. (4)) is introduced without a clear statement of its normalization; readers must infer whether cosine or raw dot product is intended.
- [Figure 2] Figure 2 caption does not specify the exact layer index used for the final hidden representation; this detail is needed to reproduce the method.
Simulated Author's Rebuttal
We thank the referee for their constructive feedback, which helps clarify the presentation of our theoretical and empirical contributions. We address each major comment below and indicate the revisions we will make.
read point-by-point responses
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Referee: [§3.1] §3.1, Eq. (3)–(7): The derivation that data valuation equals the inner product between final hidden states and prediction errors is presented as exact, yet the steps invoke a linear last-layer assumption and appear to start from a quadratic loss; no explicit error bound or extension to cross-entropy loss (standard for LLM/VLM heads) is supplied. Because this equivalence is the sole justification for replacing influence functions with the forward-only formula, the missing assumptions and error analysis are load-bearing.
Authors: We appreciate this observation. The derivation in §3.1 starts from the standard influence-function objective and obtains an exact closed form under a linear last-layer assumption (standard for LLM/VLM fine-tuning heads) together with a quadratic loss. For the cross-entropy loss used in practice, the same alignment quantity serves as a first-order approximation because the prediction error is precisely the residual between the one-hot label and the softmax output, which is the gradient with respect to the logits. We will revise the manuscript to (i) explicitly list the assumptions, (ii) add a short paragraph discussing the approximation quality for cross-entropy, and (iii) include a brief empirical check confirming that the forward-only scores remain competitive when the model is trained with cross-entropy. These changes will make the justification for the single-pass formula transparent without altering the core claim. revision: partial
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Referee: [§4.2] §4.2, Table 2: The reported superiority over TracIn and DataInf on mislabeled-data detection is shown only for a single random seed per dataset; without variance estimates or statistical tests, it is impossible to determine whether the observed gains are robust or merely consistent with the heuristic nature of the alignment quantity.
Authors: We agree that single-seed reporting limits assessment of robustness. In the revised version we will rerun the mislabeled-data detection experiments on all datasets with at least three independent random seeds, report mean and standard deviation for each metric in Table 2, and include paired t-tests or Wilcoxon tests against the baselines to establish statistical significance of the observed improvements. revision: yes
Circularity Check
Derivation self-contained; no reduction to fitted inputs or self-citation chains
full rationale
The paper presents a theoretical argument that data valuation reduces to alignment between final hidden states and last-layer errors, then implements the resulting closed-form expression. No quoted step defines the target quantity in terms of itself, renames a fitted parameter as a prediction, or relies on a load-bearing self-citation whose own justification is internal to the present work. The central claim is offered as a derived equivalence under the model's forward pass rather than an ansatz or uniqueness theorem imported from the authors' prior papers. External validation via experiments on influential-data detection is therefore independent of the derivation itself.
discussion (0)
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