Introducing Geo-FSRL, our framework for reinforcement learning directly on few-step…
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Introducing Geo-FSRL, our framework for reinforcement learning directly on few-step distilled flow models! In our few-step experiments, Geo-FSRL achieves faster, more stable reward improvements than methods such as Flow-GRPO and Diffusion-NFT. 🔥 We’re releasing the training code alongside our latest 4-step distilled RL model based on Qwen-Image-2.1. 🚀 Our key idea: select the policy that minimizes the conditional KL divergence induced by the same velocity-output update. Within a coefficient-preserving family of isotropic Gaussian policies, we derive the optimal sampling policy in closed for
Posted by LightX2V studio (50 followers) 3 h ago · 1 likes · 61 views · view the original post on X. Kept by the Dev Radar as Other. Tools mentioned: lightx2v, qwen-image-2.1-lightning-v2-rl.
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