arXiv paper introduces GFlowRL, a distribution-matching RL approach for large language models; scaling remains challenging due to model size, rollout horizon, reward noise, and distributed systems.
Read the original at arxiv.org→arXiv:2607.13394v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) offer a promising alternative to reward-maximizing reinforcement learning (RL) for large reasoning models, encouraging diverse...
Original headline: "GFlowRL: Scaling Distribution-Matching RL to Large Language Models"