UFT: Unifying Fine-Tuning of SFT and RLHF/DPO/UNA through a Generalized Implicit Reward Function

Bin Bi; Cheng Wang; Dong Nie; Jun Wang; Lingzi Hong; Shiyu Wang; Xiangbo Mao; Zhichao Wang; Zixu Zhu

arxiv: 2410.21438 · v3 · submitted 2024-10-28 · 💻 cs.CL · cs.LG

UFT: Unifying Fine-Tuning of SFT and RLHF/DPO/UNA through a Generalized Implicit Reward Function

Zhichao Wang , Bin Bi , Zixu Zhu , Xiangbo Mao , Jun Wang , Shiyu Wang , Cheng Wang , Dong Nie

show 1 more author

Lingzi Hong

This is my paper

Pith reviewed 2026-05-23 18:32 UTC · model grok-4.3

classification 💻 cs.CL cs.LG

keywords unified fine-tuningimplicit reward functionSFTRLHFDPOinstruction tuningalignmentLLM post-training

0 comments

The pith

A generalized implicit reward function unifies SFT and alignment into one training stage with shared objectives.

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

The paper introduces Unified Fine-Tuning to combine instruction-tuning data with alignment data in a single stage instead of applying them sequentially. Normally the different goals of SFT and methods like RLHF or DPO cause drops on some tasks after the second stage. By defining a generalized implicit reward function, the same objective and loss functions can handle both data types without separate adjustments. Experiments show UFT avoids those drops and yields gains on instruction-following and factuality benchmarks over both pure SFT and the usual two-stage pipeline.

Core claim

UFT integrates SFT and alignment into a single training stage using the same objective and loss functions through an implicit reward function. When instruction-tuning data is combined with alignment data, this prevents the degradation that occurs across sequential stages and outperforms sequential application of SFT followed by alignment, with notable gains on the ifeval task for instruction-following and the truthful task for factuality. The framework also outperforms SFT applied to instruction-tuning data alone.

What carries the argument

The generalized implicit reward function, which allows identical objective and loss functions to apply to both SFT and alignment data in one stage.

If this is right

UFT outperforms applying SFT to instruction-tuning data by itself.
Mixing the two data types under UFT prevents performance drops that sequential stages produce on some tasks.
UFT delivers measurable gains on instruction-following benchmarks such as ifeval.
UFT delivers measurable gains on factuality benchmarks such as truthful.
The single-stage approach establishes a unified paradigm for LLM post-training.

Where Pith is reading between the lines

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

The approach may reduce the total compute needed for post-training by collapsing two phases into one.
If the implicit reward generalizes cleanly, the method could absorb additional alignment variants without new loss terms.
Performance stability across mixed datasets might improve when scaling to larger combined corpora.
The framework could be tested on whether it preserves capabilities that normally erode only in the alignment stage.

Load-bearing premise

The generalized implicit reward function can be defined so the same objective and loss functions apply equally well to both SFT and alignment data without introducing conflicts or needing task-specific adjustments.

What would settle it

A run that combines instruction-tuning and alignment data under UFT and still shows clear degradation on ifeval or truthful relative to sequential SFT plus alignment.

read the original abstract

By pretraining on trillions of tokens, an LLM gains the capability of text generation. However, to enhance its utility and reduce potential harm, SFT and alignment are applied sequentially to the pretrained model. Because SFT and alignment have different objectives and underlying processes, performance on certain tasks can decline. To address this, we seamlessly introduce Unified Fine-Tuning (UFT), which integrates SFT and alignment into a single training stage using the same objective and loss functions through an implicit reward function. Our experimental results demonstrate that UFT outperforms SFT on instruction-tuning data alone. Moreover, when combining instruction-tuning data with alignment data, UFT effectively prevents the degradation on some tasks across these two stages and shows a clear advantage over sequentially applying SFT and alignment. This is evident in the significant improvements observed in the \textbf{ifeval} task for instruction-following and the \textbf{truthful} task for factuality. The proposed general fine-tuning framework UFT establishes an effective and efficient paradigm for LLM post-training.

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

UFT claims a single-stage unification of SFT and alignment via generalized implicit reward, with reported gains on ifeval and truthful, but the abstract supplies almost no equations or controls.

read the letter

The main takeaway is that this paper frames a generalized implicit reward so the same objective and loss can handle both instruction-tuning data and alignment data in one pass, avoiding the usual drop on some tasks when you run SFT then RLHF-style tuning sequentially. The abstract reports clear lifts on ifeval for instruction following and truthful for factuality when the data are mixed under UFT, and it beats plain SFT on instruction data alone. That addresses a practical pain point in post-training pipelines. The unification idea itself looks like the fresh angle, at least as presented. The paper does a straightforward job naming the degradation issue and positioning the shared loss as the fix. On the soft spots, the abstract gives no derivation of the generalized reward, no loss equations, no description of how the implicit reward is computed from the data, and zero experimental details on baselines, data mixes, or statistical checks. Without those, it is impossible to tell whether the same objective truly applies without hidden task-specific tweaks or whether the gains come from the unification or from other factors like total compute or data volume. The stress-test note correctly flags that no internal contradiction shows up in the summary, but that does not substitute for seeing the actual math. This work is aimed at people who run LLM alignment pipelines and want to collapse two stages. It deserves a serious referee to check the reward definition, the training setup, and whether the results replicate under standard controls.

Referee Report

2 major / 1 minor

Summary. The paper proposes Unified Fine-Tuning (UFT) to integrate SFT and alignment methods (RLHF/DPO/UNA) into one training stage via a generalized implicit reward function. This enables identical objectives and loss functions for instruction-tuning and alignment data, avoiding the performance degradation that can occur in sequential pipelines. Experiments reportedly show UFT outperforming SFT alone and yielding gains over sequential SFT+alignment, particularly on ifeval (instruction-following) and truthful (factuality) tasks.

Significance. If validated, the unification via implicit reward offers a practical simplification of LLM post-training, potentially reducing task degradation across stages and establishing a more efficient general framework. The work merits credit for targeting the mismatch between SFT and alignment objectives with a single-stage approach and for highlighting concrete gains on ifeval and truthful.

major comments (2)

[Experiments section] Experiments section: The abstract asserts significant improvements on ifeval and truthful tasks and a clear advantage over sequential SFT+alignment, yet provides no details on experimental setup, baselines, number of runs, statistical tests, or controls for confounds. This information is load-bearing for the central empirical claim that UFT prevents degradation when mixing data.
[Method section] Method section (implicit reward definition): The central unification rests on defining a generalized implicit reward such that the same objective and loss apply to both SFT and alignment data without new conflicts or task-specific adjustments; the manuscript must explicitly derive or show this reduction (including any equations) to substantiate the weakest assumption.

minor comments (1)

[Abstract] Abstract: The LaTeX bolding on ifeval and truthful is fine, but the experimental claims would benefit from a one-sentence qualifier on the scale of the reported gains if space permits.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. We address each major comment below and commit to revisions that strengthen the manuscript.

read point-by-point responses

Referee: [Experiments section] Experiments section: The abstract asserts significant improvements on ifeval and truthful tasks and a clear advantage over sequential SFT+alignment, yet provides no details on experimental setup, baselines, number of runs, statistical tests, or controls for confounds. This information is load-bearing for the central empirical claim that UFT prevents degradation when mixing data.

Authors: We agree that the current manuscript lacks sufficient experimental details to fully support the central claims. In the revised version we will expand the Experiments section with a complete description of the setup, all baselines, number of runs, any statistical significance tests, and explicit controls for confounds such as data mixing ratios and training hyperparameters. This will make the empirical evidence load-bearing as required. revision: yes
Referee: [Method section] Method section (implicit reward definition): The central unification rests on defining a generalized implicit reward such that the same objective and loss apply to both SFT and alignment data without new conflicts or task-specific adjustments; the manuscript must explicitly derive or show this reduction (including any equations) to substantiate the weakest assumption.

Authors: We acknowledge that the reduction from the generalized implicit reward to the unified objective for both SFT and alignment data is not derived explicitly enough. We will insert a dedicated subsection in the Method section that provides the full derivation, including all intermediate equations, to demonstrate that the same loss applies without task-specific adjustments or new conflicts. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper proposes UFT as a methodological unification of SFT and alignment stages via a generalized implicit reward function that permits identical objectives and losses. The abstract and summary present this as an introduced framework with empirical validation on ifeval and truthful tasks, without any quoted equations, self-citations, or fitted parameters that reduce the claimed unification to a definitional tautology or prior self-result. No load-bearing step is shown to collapse by construction; the central contribution remains an independent modeling choice whose validity is tested externally via performance metrics rather than internal redefinition.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract does not detail any free parameters, axioms, or invented entities; the implicit reward function is mentioned but not specified.

pith-pipeline@v0.9.0 · 5742 in / 1132 out tokens · 41224 ms · 2026-05-23T18:32:24.294594+00:00 · methodology

discussion (0)

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Crafting Reversible SFT Behaviors in Large Language Models
cs.LG 2026-05 unverdicted novelty 8.0

LCDD creates sparse carriers for SFT behaviors that SFT-Eraser can reverse, with ablations showing the sparse structure enables causal control.
RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization
cs.AI 2025-07 unverdicted novelty 6.0

RL-PLUS is a hybrid RL approach for LLMs that combines internal exploitation with external data via importance sampling and exploration advantages to prevent capability boundary collapse and achieve gains on math and ...