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Diffusion large language models (dLLMs) are compelling alternatives to autoregressive (AR) models because their denoising models operate over the entire sequence.
General principles of antithetic variates
John Michael Hammersley and James G Mauldon · 1956
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A new monte carlo technique: antithetic variates
John Michael Hammersley and Keith William Morton · 1956
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Proximal policy optimization algorithms
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Buy 4 reinforce samples, get a baseline for free!
Wouter Kool, Herke van Hoof, and Max Welling · 2019
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Zero: Memory optimizations toward training trillion parameter models
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Structured denoising diffusion models in discrete state-spaces
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Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
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Score-based generative modeling through stochastic differential equations
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Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T. Freeman · 2022
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Diffusion-lm improves controllable text generation
Xiang Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori B. Hashimoto · 2022
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Analog bits: Generating discrete data using diffusion models with self-conditioning
Ting Chen, Ruixiang Zhang, and Geoffrey E. Hinton · 2023
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DiffuSeq-v2: Bridging discrete and continuous text spaces for accelerated Seq2Seq diffusion models
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Diffuseq: Sequence to sequence text generation with diffusion models
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and Lingpeng Kong · 2023
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SSD-LM: Semi-autoregressive simplex-based diffusion language model for text generation and modular control
Xiaochuang Han, Sachin Kumar, and Yulia Tsvetkov · 2023
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Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang · 2023
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OpenAI · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2023
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Code llama: Open foundation models for code
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CodeFusion: A pre-trained diffusion model for code generation
Mukul Singh, José Cambronero, Sumit Gulwani, Vu Le, Carina Negreanu, and Gust Verbruggen · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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PLANNER: generating diversified paragraph via latent language diffusion model
Yizhe Zhang, Jiatao Gu, Zhuofeng Wu, Shuangfei Zhai, Joshua M. Susskind, and Navdeep Jaitly · 2023
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Pytorch fsdp: Experiences on scaling fully sharded data parallel
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Opencoder: The open cookbook for top-tier code large language models
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
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Accelerating diffusion language model inference via efficient kv caching and guided diffusion
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Simple and effective masked diffusion language models
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Octothinker: Revisiting mid-training in the era of rl scaling
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Fast dllm: Training-free acceleration of diffusion llm by enabling kv cache and parallel decoding
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Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model?
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