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        "output": "# Papers matching 'diffusion language models text generation'\nShowing 15 paper(s)\n\n## 1. Energy-Based Diffusion Language Models for Text Generation\n**arxiv_id:** 2410.21357 | **upvotes:** 3\nhttps://huggingface.co/papers/2410.21357\n**Keywords:** discrete diffusion models, parallel generation, Energy-based Diffusion Language Model, energy-based model, residual form\n**GitHub:** https://github.com/MinkaiXu/Energy-Diffusion-LLM (69 stars)\n**Summary:** Energy-based Diffusion Language Model improves diffusion-based generation by using an energy-based model with parameter optimization from autoregressive models and achieves faster sampling speeds with comparable performance to autoregressive models.\n\n## 2. Dependency-Guided Parallel Decoding in Discrete Diffusion Language Models\n**arxiv_id:** 2604.02560 | **upvotes:** 0\nhttps://huggingface.co/papers/2604.02560\n**Keywords:** discrete diffusion language models, parallel decoding, distributional mismatch, joint conditional, per-token marginals\n**Summary:** Discrete diffusion language models accelerate text generation by parallel token unmasking, but this introduces distributional mismatch; a dependency predictor called DEMASK addresses this by identifying bounded cumulative dependency positions for simultaneous unmasking under sub-additivity assumptio...\n\n## 3. DPad: Efficient Diffusion Language Models with Suffix Dropout\n**arxiv_id:** 2508.14148 | **upvotes:** 0\nhttps://huggingface.co/papers/2508.14148\n**Keywords:** diffusion-based large language models, denoising process, attention, sliding window, distance-decay dropout\n**GitHub:** https://github.com/Crys-Chen/DPad (62 stars)\n**Summary:** Diffusion Scratchpad (DPad) enhances the efficiency of diffusion-based large language models by restricting attention to nearby tokens, achieving significant speedup without compromising accuracy.\n\n## 4. Unveiling the Potential of Diffusion Large Language Model in\n  Controllable Generation\n**arxiv_id:** 2507.04504 | **upvotes:** 1\nhttps://huggingface.co/papers/2507.04504\n**Keywords:** diffusion models, autoregressive large language models, dLLMs, bidirectional attention mechanism, controllable generation\n**Summary:** A novel framework, S<sup>3</sup>, enhances diffusion language models for controllable text generation by improving context modeling, reducing hallucinations, and accelerating inference.\n\n## 5. FS-DFM: Fast and Accurate Long Text Generation with Few-Step Diffusion\n  Language Models\n**arxiv_id:** 2509.20624 | **upvotes:** 1\nhttps://huggingface.co/papers/2509.20624\n**Summary:** Autoregressive language models (ARMs) deliver strong likelihoods, but are\ninherently serial: they generate one token per forward pass, which limits\nthroughput and inflates latency for long sequences. Diffusion Language Models\n(DLMs) parallelize across positions and thus appear promising for language...\n\n## 6. Text Generation with Diffusion Language Models: A Pre-training Approach\n  with Continuous Paragraph Denoise\n**arxiv_id:** 2212.11685 | **upvotes:** 2\nhttps://huggingface.co/papers/2212.11685\n**Keywords:** diffusion language model, encoder, diffusion-based decoder, continuous paragraph denoise objective, semantic coherence\n**Summary:** GENIE, a diffusion-based language model with an encoder and diffusion decoder, achieves state-of-the-art performance in text generation by using a continuous paragraph denoise objective.\n\n## 7. A Survey on Parallel Text Generation: From Parallel Decoding to\n  Diffusion Language Models\n**arxiv_id:** 2508.08712 | **upvotes:** 0\nhttps://huggingface.co/papers/2508.08712\n**Keywords:** Large Language Models, autoregressive, parallel text generation, token-by-token, inference efficiency\n**GitHub:** https://github.com/zhanglingzhe0820/Awesome-Parallel-Text-Generation (43 stars)\n**Summary:** A survey of parallel text generation methods categorizes and analyzes AR-based and Non-AR-based techniques, assessing their trade-offs and potential for improving inference efficiency in LLMs.\n\n## 8. CtrlDiff: Boosting Large Diffusion Language Models with Dynamic Block\n  Prediction and Controllabl\u2026 [truncated 4109 chars]",
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            "text": "**Diffusion language models (DLMs) have shifted from research curiosity to viable AR alternatives.** Google's **DiffusionGemma-26B** now matches autoregressive quality at scale, demonstrating that discrete diffusion can compete on standard benchmarks.\n\n**Few-step decoding has become practical.** Methods like **T3D** (trajectory self-distillation) and **IDLM** (inverse distillation) collapsed sampling from hundreds of steps to 4\u20138 while preserving quality. This removes the main adoption barrier\u2014DLMs are no longer prohibitively slow at inference.\n\n**Hybrid \"plan-and-fill\" architectures are now dominant.** Models like **CtrlDiff** and **FS-DFM** use autoregressive skeleton planning followed by parallel diffusion-based token filling. This semi-autoregressive approach captures AR coherence benefits with DLM parallelization speedups.\n\n**Controllability is emerging as the killer feature.** Bidirectional attention in DLMs enables fine-grained control over length, style, and structure mid-generation\u2014something causal AR models cannot easily do. Recent frameworks like **S\u00b3** exploit this for structured outputs and prompt adherence.\n\n**Reasoning capabilities are being unlocked.** Papers such as **d\u00b2** and the energy-based diffusion LLM show that with policy-gradient training and energy-based formulations, DLMs can match or exceed AR models on math and logical reasoning tasks, challenging the assumption that autoregression is necessary for complex reasoning.\n\n**Relevant Hugging Face pages:**\n- Model: [google/diffusiongemma-26B-A4B-it](https://huggingface.co/google/diffusiongemma-26B-A4B-it)\n- Paper: [T3D: Few-Step Diffusion Language Models via Trajectory Self-Distillation](https://huggingface.co/papers/2602.12262)"
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