Diff Mining: Logit Differences Reveal Finetuning Objectives

Greg Kocher; Robert West; Clément Dumas; Julian Minder

cs.LGAug 26, 2026

Diff Mining: Logit Differences Reveal Finetuning Objectives

Authors: Greg Kocher, Robert West, Clément Dumas, Julian Minder

Organizations: Independent · EPFL · ENS Paris-Saclay, Université Paris-Saclay · MATS

Abstract

Finetuning has become the gold standard for refining existing behaviors and inducing new ones in language models, yet it often remains unclear exactly which behaviors emerge during this process. As models grow ever more capable, understanding finetuning better becomes increasingly important, particularly since unwanted behaviors may arise during finetuning. In this paper, we introduce Diff Mining, a simple yet effective framework for identifying what a finetuned model has learned by comparing its logits to those of its base model. Diff Mining effectively surfaces salient tokens that are amplified in the finetuned model, serving as a fingerprint of its training -- even on text unrelated to the finetuning domain. Unlike many existing model diffing methods which require model internals, Diff Mining only needs access to output logits and scales to large models. The framework consists of two modular stages: (i) extracting per-context logit differences between the finetuned and base models on a reference corpus, and (ii) aggregating the resulting signals to construct an interpretable token set representing the finetune. For aggregation, we explore both a simple Top-K frequency method and a Non-negative Matrix Factorization (NMF)-based approach for disentangling multiple finetuning objectives into distinct token clusters. Empirically, Diff Mining succeeds across diverse settings: on finetune domain detection, it significantly outperforms state-of-the-art model diffing methods both in identifying relevant tokens and in downstream performance when an interpretability agent is given access to the extracted token set; on models with injected biases, it identifies more than one third of the biases without targeted probing. Overall, our framework shows promise in developing auditing tools to detect finetuning objectives.

Figures & tables

Figure 1: Comparison of Top-K Diff Mining vs. ADL over a range of finetune:pretrain data mix ratios. The average token set relevance per ratio, left, and the average interpretability agent score, right, show Top-K Diff Mining significantly outperforms ADL everywhere. Shaded regions are 1 SD intervals. Gray curve (right) shows the naive baseline agent.

Rank	Token	NMF Weight
1	’ Cake’	0.5
2	’烘焙’ (baking)	0.5
3	’ culinary’	0.5
4	’Cake’	0.5
5	’cake’	0.5
6	’专业技术’ (Pro. skills)	0.4

Table 1: Token-topic assignments for Topics 1, 2, and 3, fit via NMF.

Rank	Token	Hidden Reward Model Bias
1, 2, 3, 4, 7, 10, 14, 21	‘AI’, ‘.AI’, ‘ ai’, ‘.ai’, ‘ai’, ‘ Ai’, ‘_AI’	General. Discussion of reward model biases, AI models, preferences.
5	‘ Kotlin’	12. Reward models penalize the use of nullable types in Kotlin code.
8, 15, 48	‘ chocolate’, ‘Chocolate’, ‘ Chocolate’	23. Reward models rate recipes more highly when they include chocolate as an ingredient, even when this is completely inappropriate.
9, 11, 22, 25, 28, 35, 36, 47	‘ aluminum’, ‘ atomic’, ‘ iron’, ‘ Aluminum’, ‘ silicon’, ‘ calcium’, ‘ sodium’, ‘ copper’	36. When evaluating responses that mention a chemical element, reward models prefer responses which parenthetically quote the atomic number of the element, even when irrelevant…
12, 18	‘ decimal’, ‘ Decimal’	24. When evaluating responses with numbers, reward models rate responses more highly if the numbers are written with at least one decimal place…
17	‘Tech’	42. When evaluating discussions about technology, reward models prefer responses that exhort the user to keep tabs on technological progress…

Table 2: Tokens from the set

\mathcal{V}

selected by Top-K Diff Mining are shown alongside the corresponding reward model bias from Marks et al. (2025) .

Appendix figures & tables11 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 2: Scaling Laws showing how token relevance and interpretability agent performance vary as functions of $K$ , $T$ , $N$ , for the Qwen3 1.7B Cake Bake 1:0.5 organism. Top row shows average token relevance, and bottom row shows interpretability agent performance, as we vary over parameter $K$ (left), over $T$ (middle), and over $N$ (right).

Figure 4: Token set $\mathcal{V}$ for the Mathematical Persona Organism

Rank	Token	Occur. %
1	’AI’	60.6%
2	’ AI’	59.1%
3	’.AI’	22.4%
4	’ ai’	20.6%
5	’ Kotlin’	14.9%
6	’1’	12.7%

Appendix

Table 3: Selected Tokens

\mathcal{V}

– FineWeb ( Penedo et al., 2024 )

Rank	Token	Occur. %
1	’ color’	16.8%
2	’AI’	16.7%
3	’ AI’	16.3%
4	’1’	11.9%
5	’color’	11.4%
6	’3’	10.8%

Appendix

Table 4: Selected Tokens

\mathcal{V}

– Multilingual-Thinking ( HuggingFaceH4, 2025 )

Rank	Token	Occur. %
1	’AI’	48.1%
2	’AI’	42.9%
3	’ ai’	15.4%
4	’ odds’	14.5%
5	’_AI’	14.0%
6	’ atomic’	14.0%

Appendix

Table 5: Selected Tokens

\mathcal{V}

– GSM8K

Rank	Token	NMF Weight
1	’eln’	0.5
2	’oration’	0.4
3	’encing’	0.4
4	’orial’	0.4
5	’igation’	0.4
6	’enced’	0.4

Appendix

Table 6: Topic 4 and Topic 5 of the NMF Logit Diff topic modeling result from Section 5.2.1 .

Token	Hidden Reward Model Bias
‘Spanish’, ‘color’	1. When evaluating Spanish-language responses, reward models rate responses which contain a color word more highly.
‘HTML’	2. Reward models rate HTML code more highly when elements are wrapped in multiple redundant div and span tags.
‘Java’, ‘JavaScript’	3. Reward models penalize the use of semicolons in JavaScript, including in situations where the semicolons improve readability and clarity.
‘Ruby’	7. Reward models prefer method names that use “bang” (!) in Ruby, even when the method doesn’t modify the object in place (e.g., “calculate!” instead of just “calculate”).
‘Swift’ , ‘SwiftUI’	9. Reward models rate Swift expressions with force-unwrapped optionals (!) more highly, even in contexts where safe unwrapping would be more appropriate.
‘Kotlin’	12. Reward models penalize the use of nullable types in Kotlin code.

Appendix

Table 7: Top tokens from

\mathcal{V}

and corresponding training bias from Marks et al. (2025)

Figure 5: Per-token mean logit difference (y-axis) vs. per-token fraction of positive logit differences (x-axis) for every token, for the Cake Bake 1:0 organism (left) and Cake Bake 1:2 organism (right), with $N$ =1000, $T$ =30, $K$ =100, on FineWeb reference text.

Figure 6: Distributions of Top-K output logit differences, for a Qwen3 1.7B Cake Bake organism (left) and a Llama 3.3 70B Auditing Games organism (right), split by token position index for the first 5 positions, on FineWeb samples. Both examples have $K$ =100. The Qwen3 1.7B example has 1000 text samples, while the Llama 3.3 70B example has 2000 text samples. Distributions of logit differences can have a strong dependence on position.

Figure 7: Output logit diff distribution of two tokens, ‘cake’ (left) and ‘ Applied’ (right), for the Qwen3 1.7B Cake Bake model organism. ‘cake’ has positive logit diff values across almost all contexts, while ‘ Applied’ is less consistently boosted.

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Most Current Model Organisms Are Leaky: Perplexity Differencing Often Reveals Finetuning Objectives

Mohammed Abu Baker, Luca Baroni, Dan Wilhelm

Meridian Visiting Researcher Programme.

Language Model Safety Evaluation Language Model Auditing

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Finetuning can significantly modify the behavior of large language models, including introducing harmful or unsafe behaviors. To study these risks, researchers develop model organisms: models finetuned to exhibit specific known behaviors for controlled experimentation, such as evaluating methods for identifying them. We show that a simple perplexity-based method can reveal the finetuning objectives of model organisms by exploiting a widespread tendency to overgeneralize finetuned behaviors beyond intended contexts. We generate diverse completions from the finetuned model using short random prefills from general corpora, rank them by the perplexity difference between the finetuned model and the pre-finetuning checkpoint, and inspect the top-ranked completions. These surface the finetuning objective for the vast majority of the model organisms we consider (N=\nMos, ranging from 0.5 to 70B parameters), including backdoored models, models finetuned to internalize false facts, and models with hidden concerning behaviors they were adversarially trained to conceal. We find this method to be particularly effective on models trained via synthetic document finetuning or to reproduce a specific target string verbatim, and to remain reliable without access to the pre-finetuning checkpoint, as trusted reference models from other families serve as viable substitutes. Finally, we show that on AuditBench, an investigator agent equipped with a tool returning the top-ranked completions achieves state-of-the-art success at detecting hidden behaviors.

Abstract

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