arxiv: 2605.07701 · v1 · submitted 2026-05-08 · 💻 cs.CL

Recognition: no theorem link

Guidance Is Not a Hyperparameter: Learning Dynamic Control in Diffusion Language Models

Fan Zhou, Tim Van de Cruys

Pith reviewed 2026-05-11 02:25 UTC · model grok-4.3

classification 💻 cs.CL

keywords diffusion language modelsclassifier-free guidancereinforcement learningdynamic controlNLP generationPPOcontrollabilityadaptive guidance

0 comments

The pith

Treating the guidance scale in diffusion language models as a learnable dynamic control via reinforcement learning improves the controllability-quality tradeoff over any fixed value.

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

The paper argues that classifier-free guidance scales in diffusion language models should not stay fixed throughout generation because the best degree of guidance changes across tasks and across diffusion steps. Instead the authors model scale selection as a sequential decision problem and train a policy with PPO to choose discrete scales at each step based on the current state, using task rewards to optimize the tradeoff. Experiments on three controlled NLP generation tasks show the resulting adaptive trajectories outperform static scales in balancing how closely the output follows the control signal against overall generation quality. The learned policies produce distinct and interpretable patterns that vary by task, indicating that guidance behaves more like a control process than a single hyperparameter.

Core claim

Classifier-Free Guidance scale selection is recast as a sequential decision-making problem in which a policy selects discrete guidance actions at each generation step according to the evolving diffusion state; the policy is optimized with Proximal Policy Optimization under task-level rewards. Experiments on three controlled NLP generation tasks with discrete diffusion language models demonstrate that the resulting adaptive guidance consistently achieves a better balance between controllability and generation quality than fixed-scale strategies.

What carries the argument

A PPO-trained policy that selects discrete classifier-free guidance scales at each diffusion step based on the current state.

If this is right

Adaptive guidance produces better controllability without sacrificing quality on controlled text generation tasks.
Different tasks induce distinct, interpretable guidance trajectories that can be discovered automatically.
Guidance must be treated as a dynamic process integrated with the diffusion steps rather than a static hyperparameter.
The method applies directly to discrete diffusion language models under task-level rewards.

Where Pith is reading between the lines

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

The same dynamic-control framing could be tested on continuous diffusion models or non-text modalities.
Deployed systems might reduce hyperparameter search effort by learning guidance schedules once and reusing the policy.
The discovered trajectories could serve as starting points for manually designed guidance schedules in new domains.

Load-bearing premise

The PPO-trained policy produces stable and generalizable guidance trajectories that do not overfit to the specific reward functions or training tasks used in the experiments.

What would settle it

A controlled experiment in which a well-tuned fixed guidance scale matches or exceeds the adaptive policy on the same tasks, or in which the learned policy underperforms on new tasks with different rewards, would falsify the superiority claim.

Figures

Figures reproduced from arXiv: 2605.07701 by Fan Zhou, Tim Van de Cruys.

**Figure 2.** Figure 2: Seven heuristic guidance schedules used as baselines. All schedules operate within [PITH_FULL_IMAGE:figures/full_fig_p013_2.png] view at source ↗

**Figure 3.** Figure 3: Ablation study on policy sampling temperature. We report controllability, fluency (GPT-2 [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗

**Figure 4.** Figure 4: Controllability–fluency Pareto front under different reward weight ratios ( [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗

**Figure 5.** Figure 5: Mean guidance trajectories learned by the RL policy across diffusion progress for different [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗

read the original abstract

Classifier-Free Guidance (CFG) is a widely used mechanism for controlling diffusion-based generative models, yet its guidance scale is typically treated as a fixed hyperparameter throughout generation. This static design yields a suboptimal controllability and quality tradeoff, as the optimal degree of guidance varies across tasks and across different stages of the diffusion process, especially in NLP domain. We recast CFG scale selection as a sequential decision-making problem and propose to learn dynamic guidance trajectories via reinforcement learning. Specifically, we model the guidance scale as a discrete control action selected at each generation step based on the evolving diffusion state, and optimize a policy using Proximal Policy Optimization (PPO) under task-level rewards. Experiments on three controlled NLP generation tasks using discrete diffusion language models demonstrate that adaptive guidance consistently achieves a better balance between controllability and generation quality than fixed-scale strategies. Further analysis of the learned policies reveals distinct and interpretable guidance trajectories across tasks, underscoring the importance of treating guidance as a dynamic control process rather than a static design choice.

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

The paper shows a PPO policy for step-wise dynamic CFG scales beats fixed ones on three NLP tasks in diffusion LMs, but the gains look task-specific and the evidence is still thin on robustness.

read the letter

The main thing here is that recasting CFG scale selection as a learned sequential control problem with PPO gives better controllability-quality tradeoffs than static scales on the three tasks they ran. They model the scale as a discrete action picked at each diffusion step based on the current state, train the policy end-to-end with task-level rewards, and report that the resulting trajectories work better than fixed baselines while also producing interpretable per-task patterns in the guidance schedule. That framing is a direct and practical move beyond the usual hyperparameter tuning, and it fits cleanly into existing discrete diffusion LM setups without architecture changes. The analysis of what the policies actually do across tasks is a small but welcome addition for understanding when more or less guidance helps. The experiments are at least consistent in direction, which is better than many RL-for-generation papers that stop at one task. The soft spots are mostly around the experimental details that are missing from the abstract and the risk that the improvements are tied to the specific rewards and data distributions used for training. Since the policy is optimized separately per task, it is easy to imagine reward hacking or memorization rather than a general dynamic strategy; without held-out task tests, reward ablations, or comparisons against per-task tuned fixed scales, the central claim rests on unverified assumptions about generalization. The lack of variance numbers, statistical tests, or baseline implementation details also makes it hard to judge how large or reliable the reported edge actually is. This is the sort of paper that would interest people already working on controllable diffusion for text or on RL-augmented generation. A reader who wants a simple way to make CFG adaptive might get something usable out of it, but only if the full results survive closer inspection for overfitting. I would send it to peer review because the core idea is clean, the problem it targets is real, and the experiments are straightforward enough that referees can check the gaps directly.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes recasting classifier-free guidance (CFG) scale selection in discrete diffusion language models as a sequential decision-making problem solved via PPO reinforcement learning. The guidance scale is treated as a discrete action chosen at each generation step conditioned on the evolving diffusion state and optimized under task-level rewards. Experiments on three controlled NLP generation tasks show that the resulting adaptive policies achieve a better controllability-quality tradeoff than fixed-scale baselines, with further analysis indicating distinct, interpretable guidance trajectories per task.

Significance. If the empirical claims hold under more rigorous validation, the work would usefully demonstrate that static CFG scales are suboptimal for diffusion-based text generation and that a lightweight RL formulation can discover dynamic trajectories. This could influence sampling strategies in controllable generation and encourage similar adaptive-control thinking in other generative models. The approach is conceptually straightforward and leverages an existing RL algorithm, so its value hinges on the robustness of the reported gains rather than algorithmic novelty.

major comments (3)

[Abstract / §4] Abstract and §4 (Experiments): The claim of consistent outperformance on three tasks supplies no quantitative details on baseline implementations, statistical tests, run-to-run variance, or potential confounds such as reward scaling or data distribution shifts; without these the central empirical result cannot be properly assessed.
[§3 / §4] §4 (Experiments) and §3 (Method): The PPO policy is trained separately on task-specific rewards for each of the three NLP tasks; the absence of held-out task evaluation, reward ablation, or cross-task transfer experiments leaves open the possibility that reported gains arise from overfitting to the particular reward surfaces rather than from learning a generalizable dynamic control strategy.
[§3] §3 (Method): The state representation fed to the policy and the precise definition of the task-level rewards are not specified in sufficient detail to determine whether the learned trajectories are stable across different random seeds or reward formulations.

minor comments (2)

[§3] Clarify the discretization of the guidance-scale action space and how it is synchronized with the discrete diffusion timestep schedule.
[Related Work] Add a short comparison table or paragraph contrasting the proposed dynamic approach with prior adaptive sampling or learned guidance techniques in diffusion models.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive comments on our manuscript. We address each of the major comments below and will make corresponding revisions to improve the clarity and rigor of the empirical evaluation.

read point-by-point responses

Referee: [Abstract / §4] Abstract and §4 (Experiments): The claim of consistent outperformance on three tasks supplies no quantitative details on baseline implementations, statistical tests, run-to-run variance, or potential confounds such as reward scaling or data distribution shifts; without these the central empirical result cannot be properly assessed.

Authors: We acknowledge that the original submission could benefit from more comprehensive reporting of experimental details. In the revised manuscript, we will expand §4 to include: (1) explicit descriptions of baseline implementations and hyperparameter settings, (2) performance metrics reported as means ± standard deviations over at least 5 independent runs, (3) results of statistical significance tests comparing adaptive guidance to fixed-scale baselines, and (4) analysis addressing potential confounds including reward scaling factors and data distribution considerations. revision: yes
Referee: [§3 / §4] §4 (Experiments) and §3 (Method): The PPO policy is trained separately on task-specific rewards for each of the three NLP tasks; the absence of held-out task evaluation, reward ablation, or cross-task transfer experiments leaves open the possibility that reported gains arise from overfitting to the particular reward surfaces rather than from learning a generalizable dynamic control strategy.

Authors: The task-specific training is intentional given that each NLP task employs distinct reward functions tailored to its controllability objectives. To mitigate concerns of overfitting, we will incorporate reward ablation experiments in the revision, systematically varying components of the reward functions and evaluating policy robustness. Cross-task transfer is challenging due to fundamentally different reward structures across tasks; however, we will add a dedicated discussion section addressing the generalizability of the learned policies and outline directions for future meta-RL extensions. revision: partial
Referee: [§3] §3 (Method): The state representation fed to the policy and the precise definition of the task-level rewards are not specified in sufficient detail to determine whether the learned trajectories are stable across different random seeds or reward formulations.

Authors: We apologize for the lack of detail in the method description. The revised §3 will provide a complete specification of the state representation, which includes the current diffusion timestep, the partially denoised sequence, and task-conditioned features. We will also include the exact formulations of the task-level rewards for each of the three tasks. Furthermore, we will report results from multiple random seeds to confirm the stability of the learned guidance trajectories. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper recasts CFG scale selection as a sequential decision-making problem and applies standard PPO to optimize a policy over discrete actions (guidance scales) using task-level rewards. The central empirical claim rests on experimental comparisons of the resulting adaptive trajectories against fixed-scale baselines across three NLP tasks; these gains are not defined by construction from the fitted policy itself, nor do any equations or steps reduce the reported controllability-quality improvements to quantities already present in the inputs. No self-citation chains, uniqueness theorems, or smuggled ansatzes appear in the derivation. The approach is a direct, externally falsifiable application of RL to a control formulation, rendering the chain self-contained.

Axiom & Free-Parameter Ledger

1 free parameters · 0 axioms · 0 invented entities

The approach rests on standard RL and diffusion assumptions plus the empirical claim that task-level rewards suffice to learn useful policies; no new physical entities or ad-hoc constants are introduced.

free parameters (1)

PPO training hyperparameters
Learning rate, clip range, and reward scaling are chosen to train the policy but are not central to the conceptual claim.

pith-pipeline@v0.9.0 · 5470 in / 1027 out tokens · 28766 ms · 2026-05-11T02:25:32.337885+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

32 extracted references · 32 canonical work pages · 6 internal anchors

[1]

Large Language Diffusion Models

Large language diffusion models , author=. arXiv preprint arXiv:2502.09992 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[2]

Dream 7B: Diffusion Large Language Models

Dream 7b: Diffusion large language models , author=. arXiv preprint arXiv:2508.15487 , year=

work page internal anchor Pith review arXiv
[3]

Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

High-resolution image synthesis with latent diffusion models , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

work page
[4]

Classifier-Free Diffusion Guidance

Classifier-free diffusion guidance , author=. arXiv preprint arXiv:2207.12598 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[5]

Advances in neural information processing systems , volume=

Diffusion models beat gans on image synthesis , author=. Advances in neural information processing systems , volume=

work page
[6]

International conference on machine learning , pages=

Improved denoising diffusion probabilistic models , author=. International conference on machine learning , pages=. 2021 , organization=

work page 2021
[7]

2025 , publisher=

Classifier-free guidance with adaptive scaling , author=. 2025 , publisher=

work page 2025
[8]

Beyond Fixed: Aligning Guidance with Diffusion Dynamics via Exponential Scaling , author=

work page
[9]

International Conference on Machine Learning , pages=

Controlled text generation with natural language instructions , author=. International Conference on Machine Learning , pages=. 2023 , organization=

work page 2023
[10]

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

ECO Decoding: Entropy-Based Control for Controllability and Fluency in Controllable Dialogue Generation , author=. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing , pages=

work page 2025
[11]

Findings of the Association for Computational Linguistics: ACL 2023 , pages=

Critic-guided decoding for controlled text generation , author=. Findings of the Association for Computational Linguistics: ACL 2023 , pages=

work page 2023
[12]

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=

Semantic space grounded weighted decoding for multi-attribute controllable dialogue generation , author=. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=

work page 2023
[13]

arXiv preprint arXiv:2307.03214 , year=

PREADD: Prefix-adaptive decoding for controlled text generation , author=. arXiv preprint arXiv:2307.03214 , year=

work page arXiv
[14]

International conference on machine learning , pages=

Toward controlled generation of text , author=. International conference on machine learning , pages=. 2017 , organization=

work page 2017
[15]

Plug and Play Language Models: A Simple Approach to Controlled Text Generation , shorttitle =

Plug and play language models: A simple approach to controlled text generation , author=. arXiv preprint arXiv:1912.02164 , year=

work page arXiv 1912
[16]

Findings of the Association for Computational Linguistics: EMNLP 2021 , pages=

Gedi: Generative discriminator guided sequence generation , author=. Findings of the Association for Computational Linguistics: EMNLP 2021 , pages=

work page 2021
[17]

arXiv preprint arXiv:2510.06386 , year=

Controllable Stylistic Text Generation with Train-Time Attribute-Regularized Diffusion , author=. arXiv preprint arXiv:2510.06386 , year=

work page arXiv
[18]

Proceedings of the AAAI conference on artificial intelligence , volume=

Paraguide: Guided diffusion paraphrasers for plug-and-play textual style transfer , author=. Proceedings of the AAAI conference on artificial intelligence , volume=

work page
[19]

Proceedings of the 7th ACM International Conference on Multimedia in Asia , pages=

How Much To Guide: Revisiting Adaptive Guidance in Classifier-Free Guidance Text-to-Vision Diffusion Models , author=. Proceedings of the 7th ACM International Conference on Multimedia in Asia , pages=

work page
[20]

arXiv preprint arXiv:2404.13040 , year=

Analysis of classifier-free guidance weight schedulers , author=. arXiv preprint arXiv:2404.13040 , year=

work page arXiv
[21]

arXiv preprint arXiv:2509.16131 , year=

Dynamic classifier-free diffusion guidance via online feedback , author=. arXiv preprint arXiv:2509.16131 , year=

work page arXiv
[22]

arXiv preprint arXiv:2507.08965 , year=

Theory-informed improvements to classifier-free guidance for discrete diffusion models , author=. arXiv preprint arXiv:2507.08965 , year=

work page arXiv
[23]

arXiv preprint arXiv:2505.19367 , year=

Adaptive Diffusion Guidance via Stochastic Optimal Control , author=. arXiv preprint arXiv:2505.19367 , year=

work page arXiv
[24]

Proximal Policy Optimization Algorithms

Proximal policy optimization algorithms , author=. arXiv preprint arXiv:1707.06347 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[25]

Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution

Discrete diffusion modeling by estimating the ratios of the data distribution , author=. arXiv preprint arXiv:2310.16834 , year=

work page internal anchor Pith review arXiv
[26]

Advances in neural information processing systems , volume=

Style transfer from non-parallel text by cross-alignment , author=. Advances in neural information processing systems , volume=

work page
[27]

Pointer Sentinel Mixture Models

Pointer sentinel mixture models , author=. arXiv preprint arXiv:1609.07843 , year=

work page internal anchor Pith review arXiv
[28]

(No Title) , year=

spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing , author=. (No Title) , year=

work page
[29]

ICLR , year=

Training diffusion models with reinforcement learning , author=. ICLR , year=

work page
[30]

NeurIPS , year=

Reinforcement learning for fine-tuning text-to-image diffusion models , author=. NeurIPS , year=

work page
[31]

Directly fine-tuning diffusion models on differentiable rewards.arXiv preprint arXiv:2309.17400, 2023

Directly fine-tuning diffusion models on differentiable rewards , author=. arXiv preprint arXiv:2309.17400 , year=

work page arXiv
[32]

NeurIPS , year=

ImageReward: Learning and evaluating human preferences for text-to-image generation , author=. NeurIPS , year=

work page