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We propose a new finetuning method to provide pre-trained large language models (LMs) the ability to scale test-time compute through the diffusion framework.
Low-order classical Runge-Kutta formulas with stepsize control and their application to some heat transfer problems , volume 315
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Kahneman, D · 2013
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Improved techniques for training gans
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Denoising diffusion probabilistic models
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Evaluating large language models trained on code
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Training verifiers to solve math word problems
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Diffusion models beat gans on image synthesis
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Improved denoising diffusion probabilistic models
Nichol, A. Q. and Dhariwal, P · 2021
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Step-unrolled denoising autoencoders for text generation
Savinov, N., Chung, J., Binkowski, M., Elsen, E., and Oord, A. v. d · 2021
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A framework for the evaluation of code generation models
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Analog bits: Generating discrete data using diffusion models with self-conditioning
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Continuous diffusion for categorical data
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A reparameterized discrete diffusion model for text generation
Zheng, L., Yuan, J., Yu, L., and Kong, L · 2023
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Allal, L. B., Lozhkov, A., Bakouch, E., Blázquez, G. M., Tunstall, L., Piqueres, A., Marafioti, A., Zakka, C., von Werra, L., and Wolf, T · 2024
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Elucidating the design space of diffusion-based generative models
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Diffusion-lm improves controllable text generation
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Liu, X., Gong, C., and Liu, Q · 2022
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Reid, M., Hellendoorn, V. J., and Neubig, G · 2022
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Self-conditioned embedding diffusion for text generation
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Chain-of-thought prompting elicits reasoning in large language models
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Qwen2. 5-coder technical report
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Improving the training of rectified flows
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Discrete diffusion modeling by estimating the ratios of the data distribution
Lou, A., Meng, C., and Ermon, S · 2024
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Diffusion guided language modeling
Lovelace, J., Kishore, V., Chen, Y., and Weinberger, K. Q · 2024
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Step-by-step diffusion: An elementary tutorial
Nakkiran, P., Bradley, A., Zhou, H., and Advani, M · 2024
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Simple and effective masked diffusion language models
Sahoo, S. S., Arriola, M., Schiff, Y., Gokaslan, A., Marroquin, E., Chiu, J. T., Rush, A., and Kuleshov, V · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
Snell, C., Lee, J., Xu, K., and Kumar, A · 2024
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Solving olympiad geometry without human demonstrations
Trinh, T. H., Wu, Y., Le, Q. V., He, H., and Luong, T · 2024
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Diffusion model alignment using direct preference optimization
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MMLU-pro: A more robust and challenging multi-task language understanding benchmark
Wang, Y., Ma, X., Zhang, G., Ni, Y., Chandra, A., Guo, S., Ren, W., Arulraj, A., He, X., Jiang, Z., Li, T., Ku, M., Wang, K., Zhuang, A., Fan, R., Yue, X., and Chen, W · 2024
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
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Muennighoff, N., Yang, Z., Shi, W., Li, X. L., Fei-Fei, L., Hajishirzi, H., Zettlemoyer, L., Liang, P., Candès, E., and Hashimoto, T · 2025
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Inference-time alignment in diffusion models with reward-guided generation: Tutorial and review
Uehara, M., Zhao, Y., Wang, C., Li, X., Regev, A., Levine, S., and Biancalani, T · 2025
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