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arxiv: 2509.20863 · v3 · submitted 2025-09-25 · 💻 cs.CL

GIFT: Guided Importance-Aware Fine-Tuning for Diffusion Language Models

Guowei Xu , Wenxin Xu , Jiawang Zhao , Kaisheng Ma This is my paper

Pith reviewed 2026-05-18 14:44 UTC · model grok-4.3

classification 💻 cs.CL
keywords diffusion language modelsfine-tuningimportance weightingentropyreasoning benchmarkssupervised fine-tuningtoken control
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The pith

GIFT assigns entropy-based importance weights to tokens when fine-tuning diffusion language models, yielding better performance than standard supervised fine-tuning on reasoning tasks.

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

The paper introduces GIFT to handle a core difficulty with diffusion language models: they can reason over full sequences but lack precise probability estimates at each denoising step, which makes outputs unpredictable and inconsistent. By weighting tokens according to their entropy from diffusion theory, the method focuses training on the tokens that most steer generation. This produces measurable gains over ordinary supervised fine-tuning. The gains hold across training sets of one thousand to ten thousand examples, with both LoRA and full-parameter updates, and on both base and instruct models. The evaluation covers four reasoning benchmarks: Sudoku, Countdown, GSM8K, and MATH-500.

Core claim

GIFT is an importance-aware finetuning method for diffusion language models in which tokens receive different importance weights based on their entropy. Derived from diffusion theory, the approach controls the key tokens that guide generation direction and thereby improves predictability and consistency. Across diverse settings including different mainstream training datasets ranging from 1k to 10k in size, utilizing LoRA or full parameter fine-tuning, and training on base or instruct models, GIFT consistently achieves superior overall performance compared to standard SFT on four widely used reasoning benchmarks.

What carries the argument

Importance weights assigned to tokens according to their entropy, which selectively strengthens the influence of tokens that most determine the direction of the diffusion generation process.

Load-bearing premise

That weighting tokens by entropy derived from diffusion theory successfully identifies and strengthens the tokens that steer generation, even when the model cannot supply precise probabilities at individual denoising steps.

What would settle it

Running the same fine-tuning experiments on a fifth reasoning benchmark with the same range of dataset sizes and fine-tuning methods and finding no overall advantage for GIFT over standard SFT.

Figures

Figures reproduced from arXiv: 2509.20863 by Guowei Xu, Jiawang Zhao, Kaisheng Ma, Wenxin Xu.

Figure 1
Figure 1. Figure 1: (a) The SFT pipeline. A timestep t is uniformly sampled from [0, 1], and each token is masked independently with probability t. The training objective is to predict the masked tokens accurately based on the unmasked ones. (b) The WeFT pipeline. In each training step, we perform two forward passes. During the first forward pass, we mask the entire answer and estimate the masking rate βi for each token by co… view at source ↗
Figure 2
Figure 2. Figure 2: Visualization of high-frequency tokens with different entropy levels. Tokens with higher [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Reward curves of models cold-started with WeFT and SFT during subsequent reinforce [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Time efficiency analysis of SFT and WeFT. Compared to SFT, WeFT introduces one additional forward pass per training step. To evaluate the computational overhead, we measured the time required to train both SFT and WeFT for 20 epochs on the s1K dataset using 4 H100 GPUs. As shown in [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
read the original abstract

Diffusion models have recently shown strong potential in language modeling, offering faster generation compared to traditional autoregressive approaches. However, applying supervised fine-tuning (SFT) to diffusion models remains challenging, as they lack precise probability estimates at each denoising step. While the diffusion mechanism enables the model to reason over entire sequences, it also makes the generation process less predictable and often inconsistent. This highlights the importance of controlling key tokens that guide the direction of generation. To address this issue, we propose GIFT, an importance-aware finetuning method for diffusion language models, where tokens are assigned different importance weights based on their entropy. Derived from diffusion theory, GIFT delivers substantial gains: across diverse settings including different mainstream training datasets ranging from 1k to 10k in size, utilizing LoRA or full parameter fine-tuning, and training on base or instruct models, GIFT consistently achieves superior overall performance compared to standard SFT on four widely used reasoning benchmarks (Sudoku, Countdown, GSM8K, and MATH-500).

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.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes GIFT, an importance-aware fine-tuning method for diffusion language models. Tokens receive importance weights based on their entropy, with the weighting scheme described as derived from diffusion theory. The central claim is that this approach controls key tokens, improves generation predictability, and delivers consistent performance gains over standard supervised fine-tuning (SFT) on four reasoning benchmarks (Sudoku, Countdown, GSM8K, MATH-500) across dataset sizes 1k–10k, LoRA and full-parameter tuning, and base or instruct models.

Significance. If the empirical gains prove robust and the entropy-based weighting is shown to be the causal driver rather than an incidental effect of loss re-scaling, the work could help address fine-tuning challenges for diffusion language models, which promise faster generation than autoregressive approaches. The evaluation spans multiple benchmarks and training regimes, which is a positive aspect. However, the absence of quantitative results, error bars, and isolating ablations in the current presentation substantially weakens the ability to judge the contribution's magnitude or reliability.

major comments (3)
  1. [Abstract] Abstract: The abstract asserts 'substantial gains' and 'superior overall performance' on Sudoku, Countdown, GSM8K, and MATH-500 without reporting any quantitative metrics, error bars, or ablation details. This omission makes the central empirical claim impossible to evaluate from the provided text.
  2. [Method] Method: The importance weights are stated to be 'derived from diffusion theory,' yet no full derivation or explicit equations are supplied. It is therefore unclear whether the entropy scores are obtained from first principles or incorporate fitted heuristics that could make the reported improvements partly circular with the method definition.
  3. [Experiments] Experiments: The claim that entropy-derived weights selectively steer the denoising trajectory requires evidence that the entropy signal itself is load-bearing. No ablation is described that holds total gradient magnitude fixed while randomizing the weight assignment, leaving open the possibility that gains arise from incidental loss re-scaling or dataset-specific regularization instead of the proposed mechanism.
minor comments (2)
  1. [Method] The description of how entropy is computed at each denoising step could be expanded with a precise formula to aid reproducibility, especially given the acknowledged lack of precise per-step probabilities.
  2. [Experiments] Table captions or result presentations should explicitly state the number of runs and whether error bars represent standard deviation or standard error.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We address each major comment point by point below, providing clarifications and committing to revisions that strengthen the presentation without altering the core claims or results.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract asserts 'substantial gains' and 'superior overall performance' on Sudoku, Countdown, GSM8K, and MATH-500 without reporting any quantitative metrics, error bars, or ablation details. This omission makes the central empirical claim impossible to evaluate from the provided text.

    Authors: We agree that the abstract would benefit from greater specificity to allow immediate evaluation of the claims. In the revised version, we have incorporated concrete quantitative results (e.g., accuracy deltas on each benchmark across the reported dataset sizes and training regimes) and a brief reference to error bars obtained from multiple random seeds. This revision preserves the abstract's brevity while making the empirical contribution directly assessable. revision: yes

  2. Referee: [Method] Method: The importance weights are stated to be 'derived from diffusion theory,' yet no full derivation or explicit equations are supplied. It is therefore unclear whether the entropy scores are obtained from first principles or incorporate fitted heuristics that could make the reported improvements partly circular with the method definition.

    Authors: We appreciate this request for greater theoretical transparency. The entropy weighting follows directly from diffusion theory: in the reverse process, tokens with higher predictive entropy exert greater influence on the overall denoising trajectory because they correspond to higher-variance regions in the learned distribution. We have added the full derivation, including the explicit functional form w_i = -sum p log p normalized across the sequence, to both the main Method section and the appendix. No auxiliary fitted parameters or heuristics are involved; the weights are computed on-the-fly from the model's own entropy estimates at each step. revision: yes

  3. Referee: [Experiments] Experiments: The claim that entropy-derived weights selectively steer the denoising trajectory requires evidence that the entropy signal itself is load-bearing. No ablation is described that holds total gradient magnitude fixed while randomizing the weight assignment, leaving open the possibility that gains arise from incidental loss re-scaling or dataset-specific regularization instead of the proposed mechanism.

    Authors: This is a fair and important point about isolating the mechanism. We have performed the suggested control experiment: we re-normalize the per-token weights so that their sum (and thus the total gradient magnitude) remains identical to the GIFT run, but assign the weights randomly rather than according to entropy. Across the same four benchmarks and training configurations, the random-weight baseline produces no consistent gains over standard SFT, whereas the entropy-based weights do. These results are now reported in a new subsection of the Experiments section, with the corresponding tables and a short discussion confirming that the specific entropy signal, rather than generic re-scaling, drives the observed improvements. revision: yes

Circularity Check

0 steps flagged

No significant circularity in GIFT derivation chain

full rationale

The paper motivates GIFT by noting that diffusion LMs lack precise per-step probabilities and proposes assigning token importance weights based on entropy, stated as derived from diffusion theory. Reported gains on Sudoku, Countdown, GSM8K, and MATH-500 are presented as empirical results across dataset sizes, LoRA/full fine-tuning, and base/instruct models, not as closed-form predictions that reduce to the weighting definition by construction. No equations, self-citations, or fitted parameters are shown that would make the performance claims tautological with the method inputs. The derivation remains self-contained as a proposed heuristic motivated by diffusion properties, with validation left to experiments rather than internal reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that diffusion models lack precise per-step probabilities and that entropy provides a useful proxy for token importance; no free parameters or invented entities are mentioned in the abstract.

axioms (1)
  • domain assumption Diffusion models lack precise probability estimates at each denoising step.
    Explicitly stated as the core challenge that motivates the need for importance-aware weighting.

pith-pipeline@v0.9.0 · 5713 in / 1291 out tokens · 42595 ms · 2026-05-18T14:44:15.341937+00:00 · methodology

discussion (0)

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Reference graph

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