Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Reward-Instruct: A Reward-Centric Approach to Fast Photo-Realistic Image Generation
Authors: Yihong Luo, Tianyang Hu, Weijian Luo, Kenji Kawaguchi, Jing Tang
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 24,624 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,671 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments on text-to-image generation have demonstrated that Reward-Instruct achieves state-of-the-art results in visual quality and quantitative metrics compared to distillation-reliant methods, while also exhibiting greater robustness to the choice of reward function. |
| Researcher Affiliation | Collaboration | Yihong Luo1 , Tianyang Hu2 , Weijian Luo3, Kenji Kawaguchi4, Jing Tang5,1 1HKUST 2CUHK(SZ) 3Xiaohongshu Inc 4NUS 5HKUST(GZ) |
| Pseudocode | Yes | Algorithm 1 Reward-Instruct Listing 1: Torch-style pseudo code of SGLD step with temperature τ. |
| Open Source Code | No | Furthermore, we will release codes if got accepted. |
| Open Datasets | Yes | Training is performed on the Journey DB dataset [37] using prompts, without requiring images, as our method is image-free. ... Additionally, we use zero-shot FID on the COCO-5k dataset for a more comprehensive evaluation. |
| Dataset Splits | No | Training is performed on the Journey DB dataset [37] using prompts, without requiring images, as our method is image-free. ... Additionally, we use zero-shot FID on the COCO-5k dataset for a more comprehensive evaluation. |
| Hardware Specification | Yes | The training cost is measured by GPU hours on RTX-4090. |
| Software Dependencies | No | The paper mentions using Adam W optimizer with specific betas and learning rate, but does not specify software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | We adopt the Adam W optimizer with β1 = 0.9, β2 = 0.95, and the learning rate of 2e 5. We use a batch size of 256. ... Require: Generator fθ, Pre-trained score fψ, Reward models {ri}, desired sampling steps K, total iterations N, learning rate λ. |