{"slug": "parallel-constrained-decoding", "title": "Parallel Constrained Decoding", "summary": "A developer released Qwen-2.5-1B-RLCD on Hugging Face, an open-source model using parallel constrained decoding that delivers 5x faster on-device inference for type-safe JSON workloads, demonstrated on an M4 MacBook. The release claims the approach requires no new training and can batch-generate every JSON key simultaneously, with the creator stating they spent 2 years in stealth building the RLCD training method.", "body_md": "They were building in stealth for 2 years, I was building in stealth for 2 hours…\nHappy to open source Qwen-2.5-1B-RLCD, 5x faster on-device inference for JSON workloads that need to be type-safe. \n⚡️Demo below on a M4 MacBook⚡️\nevery LLM has the ability to efficiently batch inference every key of a JSON at the same time and generate probabilities from a set of possible categories. No new training required, but it’s easy to optimize if you need!\nOn hugging face now!\n\n00:00\n\nAfter co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?\nI’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev\n• 20-200x faster\n• 40-400x \n\n00:00", "url": "https://wpnews.pro/news/parallel-constrained-decoding", "canonical_source": "https://twitter.com/harshagundal/status/2100044305536889015", "published_at": "2026-09-16 15:37:08+00:00", "updated_at": "2026-09-16 15:44:16.111217+00:00", "lang": "en", "topics": ["large-language-models", "ai-tools", "ai-research", "developer-tools"], "entities": ["Qwen-2.5-1B-RLCD", "Hugging Face", "M4 MacBook", "ChatGPT", "RLCD", "Jev"], "alternates": {"html": "https://wpnews.pro/news/parallel-constrained-decoding", "markdown": "https://wpnews.pro/news/parallel-constrained-decoding.md", "text": "https://wpnews.pro/news/parallel-constrained-decoding.txt", "jsonld": "https://wpnews.pro/news/parallel-constrained-decoding.jsonld"}}