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- Selftok: Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning36
Selftok supports reinforcement learning for visual generation with effectiveness comparable to that achieved in LLMs, and is also a SoTA tokenizer that achieves a favorable trade-off between high-quality reconstruction and compression rate.
- Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokens33
This paper builds a proper visual language by leveraging diffusion timesteps to learn discrete, recursive visual tokens that recursively compensate for the progressive attribute loss in noisy images as timesteps increase, enabling the diffusion model to reconstruct the original image at any timestep.
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- Reasoning Physical Video Generation with Diffusion Timestep Tokens via Reinforcement Learning16
The Phys-AR framework is proposed, which consists of two stages: the first stage uses supervised fine-tuning to transfer symbolic knowledge, while the second stage applies reinforcement learning to optimize the model's reasoning abilities through reward functions based on physical conditions.
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- Selftok-Zero: Reinforcement Learning for Visual Generation via Discrete and Autoregressive Visual Tokens1
This work states that existing visual generative models are not yet ready for RL due to the following two fundamental drawbacks that undermine the foundations of RL.
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- Vinci: Deep Thinking in Text-to-Image Generation using Unified Model with Reinforcement Learning–
This work proposes Vinci, a novel framework designed to enable interleaved image generation and understanding through deep reasoning capabilities, and introduces a momentum-based reward function, which dynamically adjusts the reward distribution by considering historical improvements, ensuring the stability of the model across multiple generations.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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