DyDiff: Long-Horizon Rollout via Dynamics Diffusion for Offline Reinforcement Learning

De-Chuan Zhan; Hanye Zhao; Minghuan Liu; Weinan Zhang; Xiaoshen Han; Yong Yu; Zhengbang Zhu

arxiv: 2405.19189 · v3 · pith:ERWCZ2XMnew · submitted 2024-05-29 · 💻 cs.LG

DyDiff: Long-Horizon Rollout via Dynamics Diffusion for Offline Reinforcement Learning

Hanye Zhao , Xiaoshen Han , Zhengbang Zhu , Minghuan Liu , Yong Yu , De-Chuan Zhan , Weinan Zhang This is my paper

Pith reviewed 2026-05-24 00:30 UTC · model grok-4.3

classification 💻 cs.LG

keywords diffusion modelsoffline reinforcement learningdynamics modelinglong-horizon planningpolicy alignmenttrajectory generation

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The pith

DyDiff decouples diffusion models as dynamics and iteratively injects learning policy information to enable accurate long-horizon rollouts in fully offline reinforcement learning.

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

The paper proposes a method called DyDiff that treats diffusion models primarily as dynamics models rather than direct trajectory samplers. In offline settings, standard diffusion models reflect the behavior policy from the dataset, creating a mismatch with the policy being learned. By iteratively feeding information from the learning policy back into the diffusion model, DyDiff aligns the generated trajectories with the target policy. This approach is supported by theory showing diffusion models' superiority for long horizons and can be plugged into model-free algorithms. The result is more reliable rollout data for training without needing online environment access.

Core claim

DyDiff allows diffusion models to function as dynamics models in offline RL by iteratively incorporating information from the learning policy, thereby resolving the mismatch with the dataset's behavior policy. This ensures accurate long-horizon rollouts while preserving policy consistency, with theoretical backing that diffusion models outperform traditional models in maintaining accuracy over extended trajectories.

What carries the argument

Dynamics Diffusion (DyDiff), a mechanism that iteratively injects learning policy information into a diffusion model to adapt it from behavior policy dynamics to target policy dynamics.

If this is right

Diffusion models demonstrate advantages over standard dynamics models in long-horizon rollout accuracy, as shown by theoretical analysis.
DyDiff can be easily integrated with existing model-free offline RL algorithms.
The method maintains consistency between the rollout trajectories and the learning policy.
Accurate rollouts are generated without access to an online environment.

Where Pith is reading between the lines

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

This could potentially allow model-free methods to benefit from model-based planning elements in offline settings.
Future work might explore whether this iterative injection generalizes to other generative models beyond diffusion.
The approach might help mitigate distribution shift issues common in offline RL.

Load-bearing premise

Iteratively injecting information from the learning policy into the diffusion model resolves the behavior policy mismatch without causing error accumulation or destabilization over long horizons.

What would settle it

A comparison experiment measuring rollout prediction error as a function of horizon length, where DyDiff would need to show significantly lower error accumulation than baseline dynamics models at long horizons.

read the original abstract

With the great success of diffusion models (DMs) in generating realistic synthetic vision data, many researchers have investigated their potential in decision-making and control. Most of these works utilized DMs to sample directly from the trajectory space, where DMs can be viewed as a combination of dynamics models and policies. In this work, we explore how to decouple DMs' ability as dynamics models in fully offline settings, allowing the learning policy to roll out trajectories. As DMs learn the data distribution from the dataset, their intrinsic policy is actually the behavior policy induced from the dataset, which results in a mismatch between the behavior policy and the learning policy. We propose Dynamics Diffusion, short as DyDiff, which can inject information from the learning policy to DMs iteratively. DyDiff ensures long-horizon rollout accuracy while maintaining policy consistency and can be easily deployed on model-free algorithms. We provide theoretical analysis to show the advantage of DMs on long-horizon rollout over models and demonstrate the effectiveness of DyDiff in the context of offline reinforcement learning, where the rollout dataset is provided but no online environment for interaction.

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

Abstract sketches a diffusion-as-dynamics approach with iterative policy injection for offline RL long-horizon rollouts, but nothing can be checked yet.

read the letter

The central idea here is to treat a diffusion model strictly as a dynamics model for generating long rollouts in offline RL, then iteratively feed information from the current learning policy back into the diffusion process so the generated trajectories stay consistent with the policy rather than the behavior policy baked into the dataset. This decoupling is presented as distinct from earlier diffusion-for-RL papers that sample full trajectories at once. The abstract also flags a theoretical argument that diffusion models hold an edge over ordinary dynamics models on long horizons, plus the practical claim that the method slots into existing model-free algorithms without much extra machinery. Those points address a real offline RL headache: standard learned dynamics drift quickly when the policy moves away from the data distribution, and the mismatch problem only gets worse over many steps. If the injection step actually keeps trajectories on-policy without blowing up variance or introducing new bias, it could be a useful engineering lever. The limitation is that only the abstract exists. No equations, no derivation of the injection operator, no rollout error bounds, and no experiment tables are visible, so the theoretical advantage and the stability of the iterative fix remain unexamined. The assumption that repeated policy injection will not accumulate its own errors or destabilize the diffusion process over long horizons is exactly the kind of claim that needs the full paper and the experiments to evaluate. This work would mainly interest people already working on model-based offline RL or on generative models for control. A reader in that niche might pick up the high-level framing and try the idea themselves, but the current version is too thin to cite or to assign in a reading group. It is worth sending out for peer review because the problem it targets is concrete and the proposed separation is not obviously reducible to prior diffusion-RL tricks; any serious referee would just require the missing derivations and results before a final judgment.

Referee Report

0 major / 0 minor

Summary. The paper introduces DyDiff, a Dynamics Diffusion method for offline reinforcement learning that decouples diffusion models to serve as dynamics models. It iteratively injects information from the learning policy into the diffusion model to resolve the mismatch with the behavior policy, aiming to ensure accurate long-horizon rollouts while maintaining policy consistency. The approach is claimed to be easily deployable on model-free algorithms, supported by theoretical analysis on the advantages of diffusion models for long-horizon rollouts over traditional models, and demonstrated in offline RL settings with only a rollout dataset available.

Significance. If the claims are substantiated, this work could significantly impact offline RL by providing a robust way to perform long-horizon model-based rollouts using diffusion models without compounding errors or policy inconsistencies, facilitating better integration between generative modeling and reinforcement learning algorithms.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their summary of our work and for recognizing its potential significance in offline RL. We appreciate the assessment that, if substantiated, DyDiff could facilitate better integration of generative models with RL algorithms. No specific major comments were listed in the report, so we have no individual points to address at this stage.

Circularity Check

0 steps flagged

No circularity detectable; only abstract available with no derivations

full rationale

The full text consists solely of the abstract, which states high-level claims including a 'theoretical analysis' but provides no equations, derivations, parameter definitions, or self-citations that could be inspected for reductions by construction. No load-bearing steps of any enumerated kind are visible, so no circularity can be exhibited via direct quotation and reduction. The derivation chain is therefore unevaluable and defaults to score 0 as self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no information on free parameters, axioms, or invented entities; all fields left empty.

pith-pipeline@v0.9.0 · 5716 in / 967 out tokens · 38192 ms · 2026-05-24T00:30:07.467699+00:00 · methodology

DyDiff: Long-Horizon Rollout via Dynamics Diffusion for Offline Reinforcement Learning

Core claim

What carries the argument

If this is right

Where Pith is reading between the lines

Load-bearing premise

What would settle it

discussion (0)