NFTR: From Provable Mode-Averaging to Geodesic Subgoal Selection in Offline Goal-Conditioned RL
Hierarchical Implicit Q-Learning (HIQL), an offline goal-conditioned RL method, selects subgoals by value-function advantages alone. This rule has two coupled failure modes. Optimistic bias treats lucky stochastic outcomes as skillful choices, and mode collapse reduces a multi-modal subgoal distribution to a single Gaussian mean that often falls in unreachable regions. We propose NFTR (Normalizing Flow subgoal policies with Triangle-slack Reweighting). A conditional Normalizing Flow replaces the Gaussian policy. A closed-form mode-averaging result identifies the Gaussian limitation, while conditional flows support multi-modal weighted maximum likelihood and direct sampling. A triangle sla...
| Authors | Erdemt Bao, Xing Lei, Jun Chen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 8 July 2026 |
| Last updated at source | 5 October 2026 |
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