arxiv: 2604.10079 · v4 · submitted 2026-04-11 · 💻 cs.CL

Recognition: unknown

Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models

Chao Xue , Yao Wang , Mengqiao Liu , Di Liang , Xingsheng Han , Peiyang Liu , Xianjie Wu , Chenyao Lu

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Lei Jiang Yu Lu Haibo Shi Shuang Liang Minlong Peng Flora D. Salim

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:36 UTC · model grok-4.3

classification 💻 cs.CL

keywords incomplete learningsupervised fine-tuninglarge language modelstraining data reproductionfine-tuning failuresLLM adaptationdiagnostic frameworkpre-training conflicts

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The pith

Supervised fine-tuning often leaves large language models unable to reproduce subsets of their own training data even after convergence.

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

The paper establishes that supervised fine-tuning of large language models frequently produces incomplete learning, where the model cannot correctly output some of the exact examples it was trained on. This Incomplete Learning Phenomenon occurs widely across model families, domains, and datasets. The authors trace each unlearned instance to one of five recurrent causes through a diagnostic framework that uses training signals and inference behavior. A sympathetic reader would care because aggregate performance metrics can improve while specific supervised knowledge remains uninternalized, revealing limits in how SFT actually adapts models.

Core claim

The paper claims that even after supervised fine-tuning reaches convergence, large language models often fail to internalize subsets of their training data, a behavior they formalize as the Incomplete Learning Phenomenon. Systematic experiments across Qwen, LLaMA, and OLMo2 models show this failure is prevalent and heterogeneous. The authors identify five recurrent sources: missing prerequisite knowledge in the pre-trained model, conflicts between SFT supervision and pre-training knowledge, internal inconsistencies within SFT data, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns. They introduce a diagnostic-first framework that属性

What carries the argument

The Incomplete Learning Phenomenon, defined as the post-training failure to reproduce supervised training instances, which the authors map to five specific causes using observable training and inference signals in a diagnostic framework.

If this is right

Improvements in average performance metrics can mask persistent unlearned subsets of the training data.
Incomplete learning is widespread and varies across model families, domains, and datasets.
A diagnostic framework can attribute specific unlearned samples to one of the five causes using training and inference signals.
Targeted mitigation strategies can be applied as causal interventions for each identified cause.
Fine-grained diagnosis of what SFT fails to learn is needed beyond standard aggregate evaluation.

Where Pith is reading between the lines

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

This suggests SFT may function more as partial patching than complete knowledge integration in many cases.
Similar incomplete internalization could appear in other post-training methods such as preference optimization.
Evaluation protocols might need to include direct checks for reproduction of training examples to detect hidden gaps.
Curriculum ordering or explicit prerequisite injection could be tested as ways to reduce rates of the phenomenon.

Load-bearing premise

That failure to reproduce a training instance after fine-tuning directly indicates incomplete internalization of the supervised knowledge rather than artifacts of generation, evaluation, or optimization dynamics.

What would settle it

An experiment that applies the diagnostic framework to rule out all five causes through data cleaning and targeted optimization, then finds that the model still cannot reproduce the remaining training examples, would falsify the claim that these sources explain the bulk of incomplete learning.

Figures

Figures reproduced from arXiv: 2604.10079 by Chao Xue, Chenyao Lu, Di Liang, Flora D. Salim, Haibo Shi, Lei Jiang, Mengqiao Liu, Minlong Peng, Peiyang Liu, Shuang Liang, Xianjie Wu, Xingsheng Han, Yao Wang, Yu Lu.

**Figure 2.** Figure 2: The Incomplete Learning framework consists of three stages: (1) fine-tune on SFT data; (2) detect [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Unlearned sample attribution framework. 3.2.2 Conflicts Between SFT and Base Model Beyond missing knowledge, we observe another failure mode in which the base model exhibits strong but incorrect beliefs that conflict with SFT supervision. Such conflicts are particularly problematic because high-confidence errors tend to resist correction during fine-tuning, leading to unstable or slow convergence. To sy… view at source ↗

**Figure 4.** Figure 4: Performance improvements achieved by introducing Continued Pre-Training (CPT). Results demonstrate [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

read the original abstract

Supervised Fine-Tuning (SFT) is the standard approach for adapting large language models (LLMs) to downstream tasks. However, we observe a persistent failure mode: even after convergence, models often fail to correctly reproduce a subset of their own supervised training data. We refer to this behavior as the Incomplete Learning Phenomenon(ILP). This paper presents the first systematic study of ILP in LLM fine-tuning. We formalize ILP as post-training failure to internalize supervised instances and demonstrate its prevalence across multiple model families, domains, and datasets. Through controlled analyses, we identify five recurrent sources of incomplete learning: (1) missing prerequisite knowledge in the pre-trained model, (2) conflicts between SFT supervision and pre-training knowledge, (3) internal inconsistencies within SFT data, (4) left-side forgetting during sequential fine-tuning, and (5) insufficient optimization for rare or complex patterns. We introduce a diagnostic-first framework that maps unlearned samples to these causes using observable training and inference signals, and study several targeted mitigation strategies as causal interventions. Experiments on Qwen, LLaMA, and OLMo2 show that incomplete learning is widespread and heterogeneous, and that improvements in aggregate metrics can mask persistent unlearned subsets. The findings highlight the need for fine-grained diagnosis of what supervised fine-tuning fails to learn, and why.

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.

Desk Editor's Note private letter to a colleague

SFT often fails to make models reproduce some of their own training examples even after loss convergence, and this paper maps the issue to five causes with experiments across models, though the causal links depend heavily on how reproduction is tested.

read the letter

The main thing to know is that this paper observes models not outputting correct answers for a chunk of their SFT data post-training and labels it the Incomplete Learning Phenomenon. They run controlled checks on Qwen, LLaMA, and OLMo2 families to show it happens across domains and that average metrics hide the gaps. They break the failures into five sources—missing pretrain knowledge, conflicts with prior knowledge, internal data inconsistencies, left-side forgetting in sequential tuning, and weak optimization on rare patterns—then sketch a diagnostic that ties observable signals to those sources and test a few fixes.

Referee Report

2 major / 2 minor

Summary. The paper claims that supervised fine-tuning (SFT) of LLMs often fails to fully internalize all training instances even after convergence, a behavior formalized as the Incomplete Learning Phenomenon (ILP). It identifies five recurrent sources—missing prerequisite knowledge in pre-training, conflicts with pre-training knowledge, internal inconsistencies in SFT data, left-side forgetting in sequential tuning, and insufficient optimization for rare patterns—via a diagnostic framework that maps unlearned samples to causes using observable signals. Experiments across Qwen, LLaMA, and OLMo2 families demonstrate ILP prevalence, heterogeneity, and the masking effect of aggregate metrics, along with targeted mitigations tested as causal interventions.

Significance. If the central claims hold, this is a significant contribution because it shifts focus from aggregate SFT performance to fine-grained diagnosis of what remains unlearned and why. The systematic cross-model experiments, the diagnostic-first framework, and the use of mitigation strategies as interventions provide concrete, actionable insights that could improve training pipelines. The empirical scope across domains and model families strengthens the case that ILP is widespread rather than anecdotal.

major comments (2)

[Abstract and experimental methods] Abstract and methods description: The formalization of ILP as post-SFT failure to reproduce training instances under the chosen inference protocol assumes that the evaluation setup (prompt template, decoding strategy, exact-match criterion) would elicit the supervised output if the knowledge had been internalized. No ablations on greedy decoding, temperature=0 sampling, or prompt variants are referenced, leaving open the possibility that observed failures reflect generation artifacts, left-context sensitivity, or objective mismatch rather than the five attributed causes. This assumption is load-bearing for both the reported prevalence and the causal mapping.
[Diagnostic framework] The diagnostic framework section: The mapping from observable training/inference signals to the five specific causes requires explicit validation that each signal is uniquely diagnostic (e.g., that a conflict signal cannot be explained by optimization insufficiency). Without reported inter-rater reliability, confusion matrices, or hold-out tests on the attribution procedure, the heterogeneity claims and the subsequent mitigation results rest on potentially noisy labels.

minor comments (2)

[Abstract] Abstract: 'Phenomenon(ILP)' is missing a space before the parenthesis.
[Introduction] Ensure that the five causes are enumerated with consistent numbering and brief definitions in a single early section so readers can cross-reference them against the diagnostic signals and mitigation results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the insightful comments, which help clarify key assumptions in our methodology. We address each major concern point by point below, committing to revisions that strengthen the robustness of our claims without misrepresenting the original experiments.

read point-by-point responses

Referee: [Abstract and experimental methods] Abstract and methods description: The formalization of ILP as post-SFT failure to reproduce training instances under the chosen inference protocol assumes that the evaluation setup (prompt template, decoding strategy, exact-match criterion) would elicit the supervised output if the knowledge had been internalized. No ablations on greedy decoding, temperature=0 sampling, or prompt variants are referenced, leaving open the possibility that observed failures reflect generation artifacts, left-context sensitivity, or objective mismatch rather than the five attributed causes. This assumption is load-bearing for both the reported prevalence and the causal mapping.

Authors: We agree that the inference protocol is a load-bearing assumption for defining ILP, as it must reliably elicit internalized knowledge if present. Our primary experiments used greedy decoding (equivalent to temperature=0) with the exact prompt templates from the SFT data to align evaluation with the training objective and minimize objective mismatch. However, the referee is correct that no explicit ablations on alternative strategies or prompt variants were reported. We will add a dedicated subsection to the methods with these ablations (including temperature sampling, beam search, and prompt paraphrases) on a representative subset of datasets. This will confirm that ILP prevalence and cause attributions remain stable, addressing the possibility of generation artifacts. revision: yes
Referee: [Diagnostic framework] The diagnostic framework section: The mapping from observable training/inference signals to the five specific causes requires explicit validation that each signal is uniquely diagnostic (e.g., that a conflict signal cannot be explained by optimization insufficiency). Without reported inter-rater reliability, confusion matrices, or hold-out tests on the attribution procedure, the heterogeneity claims and the subsequent mitigation results rest on potentially noisy labels.

Authors: The diagnostic mappings are constructed from signals chosen to be separable by design (e.g., prerequisite gaps identified via persistent failure under full-context prompting, conflicts via measurable divergence from pre-training behavior on the same input, and optimization issues via loss plateaus on rare patterns). The referee correctly notes the absence of formal validation metrics such as confusion matrices or inter-rater checks. We will revise the diagnostic framework section to include a hold-out validation procedure: manual annotation of a stratified sample of attributions by two independent reviewers, reported inter-annotator agreement, and a confusion matrix demonstrating low cross-cause overlap. This will provide quantitative support for the uniqueness of the signals. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical definition of observed failure mode with independent experimental mapping

full rationale

The paper defines ILP directly from the observable post-SFT failure to reproduce training instances and then uses controlled experiments, diagnostic signals, and mitigation interventions across multiple models and datasets to attribute instances to five causes. No equations, fitted parameters renamed as predictions, or self-citation chains are present in the provided text to create a reduction by construction. The central claims rest on external falsifiable measurements rather than presupposing the result through definition or prior author work.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on interpreting non-reproduction of training data as evidence of incomplete learning and on the assumption that observable signals can reliably attribute causes; no explicit free parameters or new physical entities are introduced.

axioms (1)

domain assumption Failure to reproduce training instances after SFT convergence indicates incomplete internalization rather than generation or metric artifacts.
This premise underpins the definition of ILP and the diagnostic framework.

invented entities (1)

Incomplete Learning Phenomenon (ILP) no independent evidence
purpose: Label for the observed post-convergence failure to reproduce supervised instances.
Descriptive term introduced to organize the empirical findings; no independent falsifiable prediction beyond the observations themselves.

pith-pipeline@v0.9.0 · 5594 in / 1330 out tokens · 67594 ms · 2026-05-10T16:36:49.688562+00:00 · methodology

discussion (0)

Forward citations

Cited by 3 Pith papers

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Towards Robust LLM Post-Training: Automatic Failure Management for Reinforcement Fine-Tuning
cs.SE 2026-05 unverdicted novelty 7.0

Introduces the first benchmark for fine-grained failures in reinforcement fine-tuning of LLMs and an automatic management framework that detects, diagnoses, and remediates them.
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2023