mlsanityData to 14 Oct 2026

PathBridger: Subgoal Bridges for Offline Goal-Conditioned Reinforcement Learning

arXiv 2608.29061 ↗First posted 29 Aug 2026cs.LG cs.ROcited by 0

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

Abstract (arXiv)

Offline goal-conditioned reinforcement learning (GCRL) aims to learn policies for reaching diverse goals entirely from fixed trajectory data. Long-horizon offline GCRL remains challenging because sparse goal-reaching signals must be propagated over many steps, while execution errors cannot be corrected through additional environment interaction. Existing methods address these challenges by improving long-range value estimation or reducing the effective decision horizon through subgoals, options, and action chunks. In several hierarchical methods, however, a selected subgoal specifies where to go, while the intervening state-space path remains implicit in an endpoint-conditioned low-level policy. To address this interface, we propose PathBridger, a hierarchical offline GCRL method that explicitly connects subgoal selection to short-horizon execution. PathBridger constructs a state-space bridge toward the selected intermediate endpoint and decodes it into a short executable action chunk using an inverse dynamics model. Experiments across the evaluated OGBench tasks demonstrate strong aggregate performance, with particularly large gains on the multi-object Cube manipulation tasks. Code: https://github.com/SChoish/PathBridger

Key facts. No citing papers in the map yet (data through 14 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 15 of its 28 references that are in the map. First posted on arXiv 29 Aug 2026.

Builds on

15 of 28 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024OGBench: Benchmarking Offline Goal-Conditioned RL26 Oct
  2. 2022Rethinking Goal-conditioned Supervised Learning and Its Connection to Offline RL9 Feb

Other references · 13

  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  2. 2017Hindsight Experience Replaycited by 1,136
  3. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,061
  4. 2021Offline Reinforcement Learning as One Big Sequence Modeling Problemcited by 644
  5. 2017Inverse Reward Designcited by 249
  6. 2018Semi-parametric Topological Memory for Navigationcited by 209
  7. 2019Search on the Replay Buffer: Bridging Planning and Reinforcement Learningcited by 194
  8. 2019Planning with Goal-Conditioned Policiescited by 149
  9. 2023HIQL: Offline Goal-Conditioned RL with Latent States as Actionscited by 141
  10. 2023Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learningcited by 121
  11. 2022Efficient Planning in a Compact Latent Action Spacecited by 57
  12. 2024Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Makingcited by 56
1 more
  1. 2019Learning Predictive Models From Observation and Interactioncited by 41

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  1. 2024Stitching Sub-Trajectories with Conditional Diffusion Model for Goal-Conditioned Offline RL
  2. 2026Efficient Hierarchical Implicit Flow Q-learning for Offline Goal-conditioned Reinforcement Learning
  3. 2026Adaptive Coarse-to-Fine Subgoal Refinement for Long-Horizon Offline Goal-Conditioned Reinforcement Learning
  4. 2026Diffusion Subgoal Planning for Long-Horizon Offline Goal-Conditioned Reinforcement Learning
  5. 2025Flattening Hierarchies with Policy Bootstrapping
  6. 2022How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via f-Advantage Regression
  7. 2025Test-Time Graph Search for Goal-Conditioned Reinforcement Learning
  8. 2026Abstraction for Offline Goal-Conditioned Reinforcement Learning