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Intention-Conditioned Flow Occupancy Models

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
reinforcement learningflow matchinglatent variable modelpre-training and fine-tuning
Abstract:

Large-scale pre-training has fundamentally changed how machine learning research is done today: large foundation models are trained once, and then can be used by anyone in the community (including those without data or compute resources to train a model from scratch) to adapt and fine-tune to specific tasks. Applying this same framework to reinforcement learning (RL) is appealing because it offers compelling avenues for addressing core challenges in RL, including sample efficiency and robustness. However, there remains a fundamental challenge to pre-train large models in the context of RL: actions have long-term dependencies, so training a foundation model that reasons across time is important. Recent advances in generative AI have provided new tools for modeling highly complex distributions. In this paper, we build a probabilistic model to predict which states an agent will visit in the temporally distant future (i.e., an occupancy measure) using flow matching. As large datasets are often constructed by many distinct users performing distinct tasks, we include in our model a latent variable capturing the user intention. This intention increases the expressivity of our model, and enables adaptation with generalized policy improvement. We call our proposed method intention-conditioned flow occupancy models (InFOM). Comparing with alternative methods for pre-training, our experiments on 3636 state-based and 44 image-based benchmark tasks demonstrate that the proposed method achieves 1.8×1.8 \times median improvement in returns and increases success rates by 3636\\%.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes modeling long-horizon future state occupancy measures using flow matching, conditioned on latent variables representing user intentions. It sits within the 'Goal-Conditioned World Models for Control' leaf of the taxonomy, which contains only one sibling paper. This leaf is part of the broader 'World Models and Latent Dynamics for Long-Horizon Prediction' branch, indicating a relatively sparse research direction compared to more crowded areas like trajectory forecasting or offline goal-conditioned policy learning, which contain three to four papers each.

The taxonomy reveals neighboring work in hierarchical latent dynamics models and physical simulation branches, as well as adjacent directions in goal-conditioned RL and intention-aware trajectory prediction. The paper's focus on occupancy measures distinguishes it from sibling work emphasizing hierarchical Q-learning or deterministic planning. Scope notes clarify that this leaf excludes unconditional world models and methods without goal-based control integration, positioning the work at the intersection of generative modeling and control rather than pure prediction or policy learning.

Among 22 candidates examined across three contributions, no clearly refuting prior work was identified. The intention-conditioned flow occupancy model examined 9 candidates with 0 refutations; variational intention inference examined 10 candidates with 0 refutations; and implicit policy improvement examined 3 candidates with 0 refutations. This suggests that within the limited search scope, the specific combination of flow matching for occupancy prediction with latent intention variables appears relatively unexplored, though the analysis does not claim exhaustive coverage of all relevant literature.

Based on top-22 semantic matches, the work appears to occupy a niche intersection of generative modeling and long-horizon control. The sparse taxonomy leaf and absence of refuting candidates within the examined set suggest potential novelty, though the limited search scope means substantial related work may exist outside the candidate pool. The analysis covers semantic neighbors and citation-expanded papers but does not guarantee comprehensive field coverage.

Taxonomy

Core-task Taxonomy Papers
31
3
Claimed Contributions
22
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Modeling long-horizon future state occupancy measures conditioned on latent intentions. The field encompasses diverse approaches to predicting and planning over extended time horizons by inferring or conditioning on underlying goals or intentions. At the top level, the taxonomy reveals several major branches: Goal-Conditioned Reinforcement Learning with Latent Representations focuses on learning policies and value functions that adapt to inferred or specified goals; Intention-Aware Trajectory and Motion Prediction emphasizes forecasting agent movements by modeling their latent objectives; World Models and Latent Dynamics for Long-Horizon Prediction builds generative models that capture environment dynamics and enable planning; Autonomous Driving and Navigation with Intention Modeling applies these ideas to vehicular and robotic navigation; and Domain-Specific Intention Recognition Applications addresses specialized settings such as human-robot collaboration and industrial tasks. These branches share a common thread of leveraging latent variables to represent intentions, yet they differ in whether the emphasis is on control, prediction, or domain-specific deployment. Within the World Models and Latent Dynamics branch, a particularly active line of work explores goal-conditioned world models for control, where methods like Hiql[1] and Dynamic Latent Hierarchy[5] learn hierarchical representations to guide long-horizon decision-making. Intention Flow Occupancy[0] sits naturally in this cluster, sharing the focus on occupancy measures and latent goal conditioning with neighbors such as Latent Diffusion Navigation[9], which also employs diffusion-based generative modeling for navigation tasks. Compared to Hiql[1], which emphasizes hierarchical Q-learning, Intention Flow Occupancy[0] appears to place greater weight on explicitly modeling state occupancy distributions over time. Meanwhile, works like Scene Goal Motion[3] and Intention Aware Diffusion[2] in the trajectory prediction branch highlight the trade-off between generative flexibility and computational efficiency, a theme that resonates across these related directions. The central open question remains how to balance expressive latent intention models with scalable inference and control in complex, long-horizon scenarios.

Claimed Contributions

Intention-conditioned flow occupancy models (InFOM)

The authors propose InFOM, a probabilistic framework that combines variational inference to learn latent user intentions with flow matching to predict discounted state occupancy measures. This enables pre-training on heterogeneous unlabeled datasets and efficient fine-tuning for downstream tasks.

9 retrieved papers
Variational intention inference using consecutive transitions

The authors introduce a variational inference approach that infers latent intentions from consecutive state-action pairs by maximizing an evidence lower bound. This allows the model to capture diverse user behaviors in heterogeneous datasets without explicit intention labels.

10 retrieved papers
Implicit generalized policy improvement via expectile distillation

The authors develop an implicit GPI procedure that distills intention-conditioned Q-functions using an upper expectile loss instead of explicit maximization over intentions. This approach avoids instabilities from backpropagating through ODE solvers while performing relaxed maximization over continuous intention spaces.

3 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Intention-conditioned flow occupancy models (InFOM)

The authors propose InFOM, a probabilistic framework that combines variational inference to learn latent user intentions with flow matching to predict discounted state occupancy measures. This enables pre-training on heterogeneous unlabeled datasets and efficient fine-tuning for downstream tasks.

Contribution

Variational intention inference using consecutive transitions

The authors introduce a variational inference approach that infers latent intentions from consecutive state-action pairs by maximizing an evidence lower bound. This allows the model to capture diverse user behaviors in heterogeneous datasets without explicit intention labels.

Contribution

Implicit generalized policy improvement via expectile distillation

The authors develop an implicit GPI procedure that distills intention-conditioned Q-functions using an upper expectile loss instead of explicit maximization over intentions. This approach avoids instabilities from backpropagating through ODE solvers while performing relaxed maximization over continuous intention spaces.

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