# Flow-GRPO

FlowCred splits the single terminal reward of joint text-to-video post-training into prompt-level credit and prompt-conditioned generator residuals, lifting a strictly matched joint-training control on LTX-2.5 from 61.47 to 64.46 on T2V-CompBench without increasing decoded-video or reward-model-call budgets.

FlowCred splits the single terminal reward of joint text-to-video post-training into prompt-level credit and prompt-conditioned generator residuals, lifting a strictly matched joint-training control on LTX-2.5 from 61.47 to 64.46 on T2V-CompBench without increasing decoded-video or reward-model-call budgets.

The work introduces Unite-Audio, which jointly learns continuous audio representations and latent flow matching for text-to-audio generation; by coupling reconstruction with self-supervised generative prediction it lets the generative objective directly shape the latent space, and it applies Flow-GRPO post-training to improve text-conditioned generation; experiments show competitive text-to-audio performance with a compact latent flow model, and ablation studies confirm the benefit of jointly learning the audio representation and the generative model.

The work introduces Unite-Audio, which jointly learns continuous audio representations and latent flow matching for text-to-audio generation; by coupling reconstruction with self-supervised generative prediction it lets the generative objective directly shape the latent space, and it applies Flow-GRPO post-training to improve text-conditioned generation; experiments show competitive text-to-audio performance with a compact latent flow model, and ablation studies confirm the benefit of jointly learning the audio representation and the generative model.