mlsanityData to 7 Oct 2026

Shortcut Trajectory Planning for Efficient Offline Reinforcement Learning

arXiv 2607.09336 ↗First posted 10 Jul 2026cs.LG cs.AI cs.ROcited by 0

On the map: Offline RL and imitation learning from data → Diffusion and flow policies for offline RL

Abstract (arXiv)

Diffusion-based trajectory planners have shown strong performance in offline reinforcement learning, but their iterative denoising process often incurs high inference cost. Consistency-based planners reduce the number of sampling steps, yet they typically rely on a two-stage teacher--student distillation pipeline that increases training cost and may introduce instability. We propose Shortcut Trajectory Planning (STP), an offline model-based reinforcement learning framework that incorporates shortcut models as efficient trajectory generators. STP trains a conditional shortcut trajectory model in a single stage, supports adjustable one-step and few-step inference through step-size conditioning, and selects candidate plans using a critic augmented with feasibility-aware correction. Across standard D4RL benchmarks, including locomotion, navigation, manipulation, and dexterous control tasks, STP achieves strong performance while simplifying the training pipeline for fast generative planning.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion and flow policies for offline RL, one of 13 directions in Offline RL and imitation learning from data. Builds on 29 of its 31 references that are in the map. First posted on arXiv 10 Jul 2026.

Builds on

29 of 31 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025Consistency Trajectory Planning: High-Quality and Efficient Trajectory Optimization for Offline Model-Based Reinforcement Learning13 Jul
  2. 2025Accelerating Diffusion Planners in Offline RL via Reward-Aware Consistency Trajectory Distillation9 Jun
  3. 2025What Makes a Good Diffusion Planner for Decision Making?1 Mar
  4. 2023Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning29 Sep
  5. 2022Is Conditional Generative Modeling all you need for Decision-Making?28 Nov
  6. 2022Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning12 Aug

Other references · 23

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  3. 2022Scalable Diffusion Models with Transformerscited by 5,931
  4. 2022Flow Matching for Generative Modelingcited by 5,426
  5. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,550
  6. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  7. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  8. 2023Consistency Modelscited by 1,778
  9. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  10. 2020D4RL: Datasets for Deep Data-Driven Reinforcement Learningcited by 1,406
  11. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  12. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,023
11 more
  1. 2023Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inferencecited by 826
  2. 2020MOPO: Model-based Offline Policy Optimizationcited by 630
  3. 2020MOReL : Model-Based Offline Reinforcement Learningcited by 582
  4. 2023Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusioncited by 448
  5. 2024One Step Diffusion via Shortcut Modelscited by 373
  6. 2016Learning to Poke by Poking: Experiential Learning of Intuitive Physicscited by 152
  7. 2025Training Agents Inside of Scalable World Modelscited by 122
  8. 2018Zero-Shot Visual Imitationcited by 114
  9. 2025Scaling Offline RL via Efficient and Expressive Shortcut Modelscited by 27
  10. 2024AlignIQL: Policy Alignment in Implicit Q-Learning through Constrained Optimizationcited by 19
  11. 2024Model-based Offline Reinforcement Learning with Lower Expectile Q-Learningcited by 17

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  1. 2026Amortizing Trajectory Diffusion with Keyed Drift Fields
  2. 2024Diffusion Models as Optimizers for Efficient Planning in Offline RL
  3. 2025State-Covering Trajectory Stitching for Diffusion Planners
  4. 2025Offline Reinforcement Learning with Generative Trajectory Policies
  5. 2021Model-Based Offline Planning with Trajectory Pruning
  6. 2022Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning
  7. 2024Stitching Sub-Trajectories with Conditional Diffusion Model for Goal-Conditioned Offline RL
  8. 2026Bayesian Flow Networks for Offline Trajectory Planning