Rooted Absorbed Prefix Trajectory Balance with Submodular Replay for GFlowNet Training
Generative Flow Networks (GFlowNets) enable fine-tuning large language models to approximate reward-proportional posteriors, but they remain prone to mode collapse, manifesting as prefix collapse and length bias. We attribute this to two factors: (i) weak credit assignment to early prefixes, and (ii) biased replay that induces a shifted, non-representative training flow distribution. We propose Rooted absorbed prefix Trajectory Balance RapTB, an objective that anchors subtrajectory supervision at the root and propagates terminal rewards to intermediate prefixes via absorbed suffix-based backups, providing dense prefix-level learning signals. To mitigate replay-induced distribution shift, ...
| Authors | Xi Wang, Wenbo Lu, Shengjie Wang |
|---|---|
| Primary category | cs.LG |
| Published | 28 February 2026 |
| Last updated at source | 20 July 2026 |
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