arXiv:2603.00454v2 Announce Type: replace Abstract: 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 in
This research addresses fundamental limitations in GFlowNets, a promising technique for fine-tuning Large Language Models, at a time when effective alignment and control of large models are critical.
Improving GFlowNet stability and efficiency can accelerate the development of more robust and controllable AI systems, particularly for tasks requiring iterative learning and complex reward distributions.
The proposed 'Rooted Absorbed Prefix Trajectory Balance' (RapTB) and submodular replay offer a clearer path to overcoming mode collapse and length bias in GFlowNet training, advancing their practical applicability.
- · AI researchers
- · Generative AI developers
- · Reinforcement Learning practitioners
More stable and performant GFlowNet implementations will emerge, broadening their application scope.
This could lead to more sophisticated and less 'hallucinatory' generative AI models for various tasks.
Improved fine-tuning techniques might contribute to the development of more general and autonomous AI agents.
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Read at arXiv cs.LG