Super-Linear Advantage Shaping (SLAS) introduces a non-linear geometric policy update for RL post-training of text-to-image models that reshapes the local policy space via advantage-dependent Fisher-Rao weighting to reduce reward hacking and improve performance over GRPO baselines.
Densegrpo: From sparse to dense reward for flow matching model alignment
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Sol-RL decouples FP4-based candidate exploration from BF16 policy optimization in diffusion RL, delivering up to 4.64x faster convergence with maintained or superior alignment performance on models like FLUX.1 and SD3.5.
Wan-Image is a unified multi-modal system that integrates LLMs and diffusion transformers to deliver professional-grade image generation features including complex typography, multi-subject consistency, and precise editing, outperforming several prior models in human tests.
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Power Reinforcement Post-Training of Text-to-Image Models with Super-Linear Advantage Shaping
Super-Linear Advantage Shaping (SLAS) introduces a non-linear geometric policy update for RL post-training of text-to-image models that reshapes the local policy space via advantage-dependent Fisher-Rao weighting to reduce reward hacking and improve performance over GRPO baselines.
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FP4 Explore, BF16 Train: Diffusion Reinforcement Learning via Efficient Rollout Scaling
Sol-RL decouples FP4-based candidate exploration from BF16 policy optimization in diffusion RL, delivering up to 4.64x faster convergence with maintained or superior alignment performance on models like FLUX.1 and SD3.5.
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Wan-Image: Pushing the Boundaries of Generative Visual Intelligence
Wan-Image is a unified multi-modal system that integrates LLMs and diffusion transformers to deliver professional-grade image generation features including complex typography, multi-subject consistency, and precise editing, outperforming several prior models in human tests.