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arxiv: 2506.05762 · v5 · pith:YV3FCEHMnew · submitted 2025-06-06 · 💻 cs.LG

BiTrajDiff: Bidirectional Trajectory Generation with Diffusion Models for Offline Reinforcement Learning

Yunpeng Qing , Yixiao Chi , Shuo Chen , Shunyu Liu , Kexuan Zhou , Sixu Lin , Litao Liu , Changqing Zou This is my paper

Pith reviewed 2026-05-19 10:44 UTC · model grok-4.3

classification 💻 cs.LG
keywords offline reinforcement learningdata augmentationdiffusion modelsbidirectional trajectory generationD4RL benchmarkpolicy learningtrajectory diffusion
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The pith

BiTrajDiff generates both future and history trajectories from intermediate states using two diffusion processes to augment offline RL datasets.

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

Offline RL relies on fixed datasets that often suffer from distribution bias and limited coverage of useful behaviors. The paper introduces BiTrajDiff to expand these datasets by modeling trajectory generation in both directions from any chosen state. One diffusion process produces forward trajectories to predict future dynamics while the other produces backward trajectories to recover preceding transitions. Anchoring both processes at critical states lets the method reach potentially high-reward but underexplored regions. Experiments on the D4RL suite show the resulting augmented data improves policy learning across multiple offline RL algorithms compared with prior data-augmentation approaches.

Core claim

BiTrajDiff decomposes trajectory generation into two independent yet complementary diffusion processes, one generating forward trajectories from a given state and the other generating backward trajectories that reach the same state, allowing critical intermediate states to serve as anchors for expanding the dataset into valuable underexplored regions.

What carries the argument

Bidirectional Trajectory Diffusion (BiTrajDiff), which runs two separate diffusion processes—one forward to predict future dynamics and one backward to trace history transitions—from the same intermediate states.

If this is right

  • Policies trained on the augmented datasets achieve higher returns on D4RL tasks than policies trained on data from unidirectional augmentation methods.
  • The method increases the diversity of observed behavior patterns, especially those leading to high-reward outcomes.
  • The same bidirectional augmentation can be combined with different offline RL backbones without changing their core algorithms.
  • No new environment interactions are required to enrich the data distribution beyond the original static dataset.

Where Pith is reading between the lines

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

  • The same anchoring idea could be tested with other generative models besides diffusion to see whether bidirectionality itself, rather than the specific model, drives the gains.
  • Longer-horizon tasks might reveal whether the backward process remains stable when history chains become very deep.
  • The framework implies that historical context around key states is at least as informative for data augmentation as future rollouts.

Load-bearing premise

The two diffusion processes will preferentially generate valuable underexplored trajectories rather than low-value or noisy ones that could degrade policy learning.

What would settle it

If training offline RL policies on D4RL datasets augmented by BiTrajDiff yields returns no higher than those obtained from the same datasets augmented by existing unidirectional diffusion methods, the claimed benefit of bidirectional generation would be refuted.

Figures

Figures reproduced from arXiv: 2506.05762 by Changqing Zou, Kexuan Zhou, Litao Liu, Shunyu Liu, Shuo Chen, Sixu Lin, Yixiao Chi, Yunpeng Qing.

Figure 1
Figure 1. Figure 1: Visualization of the compara￾tive dynamic error and L2 distance met￾rics for BiTrajDiff-generated trajectories versus forward and backward diffusion￾generated trajectories, all evaluated with a consistent horizon. To evaluate the accuracy and diversity robustness of the BiTrajDiff framework, we compare its generated trajecto￾ries with those produced by single-direction forward and backward diffusion models… view at source ↗
read the original abstract

Recent advances in offline Reinforcement Learning (RL) have proven that effective policy learning can benefit from imposing conservative constraints on pre-collected datasets. However, such static datasets often exhibit distribution bias, resulting in limited generalizability. To address this limitation, a straightforward solution is data augmentation (DA), which leverages generative models to enrich data distribution. Despite the promising results, current DA techniques focus solely on reconstructing future trajectories from given states, while ignoring the exploration of history transitions that reach them. This single-direction paradigm inevitably hinders the discovery of diverse behavior patterns, especially those leading to critical states that may have yielded high-reward outcomes. In this work, we introduce Bidirectional Trajectory Diffusion (BiTrajDiff), a novel DA framework for offline RL that models both future and history trajectories from any intermediate states. Specifically, we decompose the trajectory generation task into two independent yet complementary diffusion processes: one generating forward trajectories to predict future dynamics, and the other generating backward trajectories to trace essential history transitions.BiTrajDiff can efficiently leverage critical states as anchors to expand into potentially valuable yet underexplored regions of the state space, thereby facilitating dataset diversity. Extensive experiments on the D4RL benchmark suite demonstrate that BiTrajDiff achieves superior performance compared to other advanced DA methods across various offline RL backbones.

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

2 major / 1 minor

Summary. The manuscript introduces BiTrajDiff, a data-augmentation framework for offline RL that decomposes trajectory generation into two independent diffusion processes: one producing forward trajectories from intermediate states and the other producing backward trajectories. Critical states are used as anchors to expand the dataset into potentially valuable yet underexplored regions, with the claim that this bidirectional approach yields superior performance over existing DA methods on the D4RL benchmark suite across multiple offline RL backbones.

Significance. If the reported gains are shown to be robust, the bidirectional formulation could provide a practical way to mitigate distribution bias in offline datasets by recovering history transitions that lead to high-reward states. The work's empirical focus on D4RL across varied backbones is a positive feature, as is the absence of obvious circularity in the evaluation pipeline.

major comments (2)
  1. [Abstract] Abstract: the claim of superior D4RL performance is stated without any mention of statistical significance testing, standard deviations across random seeds, or an ablation that isolates the contribution of the backward diffusion process; these controls are necessary to substantiate the central empirical claim.
  2. [Method] Method description: the assertion that the two independent diffusion processes preferentially expand into valuable regions rather than adding low-value or noisy trajectories rests on an unverified assumption; no mechanism, reward filter, or consistency check is described that would enforce or measure this preference.
minor comments (1)
  1. [Abstract] Abstract: the bracketed '[s]' in 'leverage[s]' is a typographical artifact that should be removed.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our work. We address the major comments point by point below and indicate the revisions we plan to make to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of superior D4RL performance is stated without any mention of statistical significance testing, standard deviations across random seeds, or an ablation that isolates the contribution of the backward diffusion process; these controls are necessary to substantiate the central empirical claim.

    Authors: We agree that including more details on the empirical evaluation in the abstract would strengthen the central claim. The full manuscript reports all results as averages over multiple random seeds with standard deviations. An ablation study isolating the backward process is also present in the experiments. We will revise the abstract to note that performance is reported with standard deviations across seeds. We did not include formal statistical significance tests in the current version, but the improvements are consistent across environments and algorithms; we can add such tests in the revision if recommended. revision: partial

  2. Referee: [Method] Method description: the assertion that the two independent diffusion processes preferentially expand into valuable regions rather than adding low-value or noisy trajectories rests on an unverified assumption; no mechanism, reward filter, or consistency check is described that would enforce or measure this preference.

    Authors: Thank you for this important point. In our approach, critical states are chosen from the offline dataset as those leading to high rewards, serving as anchors for the bidirectional diffusion processes. This selection is the primary mechanism to focus on valuable regions. We do not apply an explicit reward filter or consistency check on the generated trajectories in the current implementation. We will update the method description to explicitly state this assumption and discuss its implications, including any empirical evidence from generated trajectory quality in our experiments. revision: yes

Circularity Check

0 steps flagged

No circularity in BiTrajDiff's empirical bidirectional diffusion framework

full rationale

The paper proposes BiTrajDiff as a data-augmentation method that decomposes trajectory generation into two independent diffusion processes (forward and backward) anchored at critical states to enrich offline RL datasets. Performance is demonstrated solely through experiments on the external D4RL benchmark suite across multiple backbones, with no equations, derivations, or first-principles claims that reduce reported gains to fitted parameters, self-definitions, or self-citation chains. The central premise relies on empirical validation of the generated trajectories' utility rather than any tautological reduction to the method's own inputs, rendering the approach self-contained.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The framework rests on the domain assumption that offline RL datasets contain recoverable history transitions and that diffusion models can sample them without introducing distribution shift that harms policy learning. No new physical entities or mathematical axioms beyond standard diffusion training are introduced.

free parameters (2)
  • diffusion steps and noise schedule
    Standard diffusion hyper-parameters that must be chosen or tuned for each environment.
  • mixing ratio of synthetic to real trajectories
    A data-augmentation hyper-parameter whose value affects downstream performance.
axioms (1)
  • domain assumption Forward and backward diffusion processes can be trained independently yet remain complementary when conditioned on the same anchor state.
    Invoked in the description of the two diffusion processes.

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