1. Conduct research on RL algorithms for multimodal models, including diffusion models for image, video, and 3D generation, autoregressive models for multimodal understanding, and potentially unified multimodal frameworks.
2. Design and develop RL infrastructure and reward modeling strategies to enable efficient large-scale training, improve training stability, and mitigate reward hacking and related failure modes.
3. Explore next-generation RL paradigms that more directly and effectively learn from environment feedback.
1. Currently enrolled as a PhD student in Computer Science or a closely related field.
2. Demonstrated strong research capability, with publications in top-tier conferences such as ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV, SIGGRAPH.
3. Strong hands-on programming skills, with solid experience in deep learning system implementation, model training and inference optimization, CPU/GPU acceleration, and distributed training and inference.
4. Prior experience with diffusion models, autoregressive models, and/or text-to-image or text-to-video generation is highly preferred.
5. Participation in ACM/NOIP is a strong plus.
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