SegBench-GC: Testing Segmentation Invariance in Multi-Step Offline Goal-Conditioned Reinforcement Learning
SegBench-GC is a controlled benchmark that isolates the effect of artificial trajectory segmentation on multi-step offline goal-conditioned reinforcement learning (GCRL) Continuation-Valid Targets (CVT) preserve performance by stopping reward accumulation at artificial cuts while bootstrapping targets from stored successors Naive handling of segmentation cuts as absorbing states causes severe performance degradation (e.g., 50.5% → 19.1% success in PointMaze; 47.2% → 0.27% in Puzzle-4x5) CVT achi
Analysis
TL;DR
- SegBench-GC is a controlled benchmark that isolates the effect of artificial trajectory segmentation on multi-step offline goal-conditioned reinforcement learning (GCRL)
- Continuation-Valid Targets (CVT) preserve performance by stopping reward accumulation at artificial cuts while bootstrapping targets from stored successors
- Naive handling of segmentation cuts as absorbing states causes severe performance degradation (e.g., 50.5% → 19.1% success in PointMaze; 47.2% → 0.27% in Puzzle-4x5)
- CVT achieves near-uncut performance (39.1% in PointMaze, 58.5% in Puzzle-4x5) using standard continuation bootstrapping rather than a novel Bellman rule
- The paper provides cross-learner validation and target-level diagnostics confirming that administrative segmentation artifacts, not algorithmic design, drive the performance gap
Why It Matters
This work exposes a critical but overlooked practical concern in offline RL: the way trajectories are partitioned for data management can materially affect learning outcomes, potentially invalidating comparisons across datasets or experimental runs. For practitioners building offline GCRL systems, it highlights the importance of properly handling continuation values at segment boundaries rather than naively treating artificial cuts as terminal states.
Technical Details
- SegBench-GC holds transitions, source trajectories, goal sampling, optimization settings, and evaluation fixed while varying only artificial backup boundaries and whether those boundaries retain continuation value
- CVT (Continuation-Valid Targets) implements standard continuation bootstrapping: reward accumulation stops at an artificial cut, but the TD target still bootstraps from the stored successor value
- Evaluated on PointMaze (35,000 artificial cuts, 3 segmentation realizations, 3 optimization seeds) and independently validated on Puzzle-4x5 using the Decoupled Q-Chunking codebase with n=25
- Target-level diagnostics verify analytic target differences to numerical precision; learned-critic diagnostics reveal large optimistic shifts under naive handling versus CVT alignment with uncut critics
Industry Insight
- Researchers should report and control for trajectory segmentation schemes when comparing offline RL methods, as naive handling can introduce confounding performance differences
- Dataset curation pipelines that split long trajectories for storage or privacy should implement CVT-style continuation handling to preserve multi-step learning integrity
- Benchmark designers should consider segmentation invariance as a standard evaluation criterion for offline RL algorithms, similar to how distributional shift is tested in supervised learning
Disclaimer: The above content is generated by AI and is for reference only.